Sep 28, 2018 | Volume 8 - Issue 3
Nancy E. Thacker, Jillian M. Blueford
Counselors are becoming more involved with clients pursuing physician-assisted death (PAD) as legislation for legalization increases. PAD may present complex values-based conflicts that can challenge counselors to maintain ethical practice in counseling. When conflicts arise, counselors must engage in ethical decision making that considers systemic influences on personally held beliefs and values. The authors merge ecological systems theory with the counselor values-based conflict model to offer a holistic approach to resolving values-based conflicts surrounding PAD. In this article, the authors review PAD and counselors’ roles in the hastened death process, discuss sources and impacts of personal and professional values through an ecological systems lens, and provide an applied method of managing values-based conflicts with PAD through a case illustration.
Keywords: physician-assisted death, hastened death, values-based conflict, ethical decision making, ecological systems
Individuals with terminal illnesses encounter difficult end-of-life decisions amidst experiencing physical and emotional distress (Daneker, 2006). Currently in six U.S. states and Washington, DC, terminally ill individuals have a legal right to end their lives via physician-assisted death (PAD). As legislation for legalization of PAD increases, more terminally ill patients can consider PAD as an option (Miller, Hedlund, & Soule, 2006). As a result, the need for mental health professionals to assist individuals dealing with these end-of-life decisions is on the rise.
The topic of death presents complex questions about the meaning of life and death and evokes reflections on one’s personal beliefs and values surrounding death and dying (Yalom, 2009). Terminally ill individuals may confront their personal beliefs about a morally just or good death, explore feelings about the process of dying, and consider their levels of personal control or power in their processes of dying (Laakkonen, Pitkala, & Strandberg, 2004; Yalom, 2008). Religion and spirituality often contribute to terminally ill individuals’ beliefs and values surrounding death and dying and can influence end-of-life decisions (Reiner, 2007). Each personal belief and value is influenced by systemic factors, cultural experiences, and cultural customs or expectations that play a role in end-of-life decision making (Laakkonen et al., 2004; Neimeyer, Klass, & Dennis, 2014).
Counselors will confront their beliefs and values about death and dying as terminally ill individuals who are contemplating PAD (PAD clients) seek counseling to explore end-of-life decisions (Werth & Crow, 2009). If counselors’ beliefs and values conflict with PAD clients’ beliefs and values, or PAD itself, then it may present an ethical dilemma that challenges the quality of care counselors provide (Heller Levitt & Hartwig Moorhead, 2013). Although not all counselors may experience a value conflict related to PAD, those who do experience a conflict may look to the American Counseling Association’s (ACA) Code of Ethics (2014) and an ethical decision-making model that accurately addresses the values-based nature of the ethical dilemma at hand.
Multiple scholars have discussed the need to explore values related to personal conflicts to maintain ethical practice in counseling (Cottone & Tarvydas, 2016). However, few sources have yet to provide direction for counselors on how to resolve personal values-based conflicts regarding PAD. There is an added layer of difficulty with PAD clients because of the multifaceted nature of personal and professional values at play. Counselors are grounded on the ethical principles of promoting client autonomy and respecting cultural differences in decisions (ACA, 2014), but hastening death conflicts with the counseling profession’s inherent stance to “first do no harm” and to maintain client safety and preserve life when clients desire to end their lives (Cohen, 2001). Even though hastening death is legal in certain states, values surrounding the decision to end life do not simply cease because there is justified reasoning for a decision. Thus, counselors face a challenging dichotomy between law and values in their practice with PAD clients.
Recent changes in the counseling profession’s ethical code also contribute to the potential challenge of maintaining ethical practice with PAD clients. The ACA Code of Ethics (2005) included codes that addressed counseling practice with clients considering end-of-life options. Section A.9 in the ACA Code of Ethics (2005) provided guidelines about the quality of care counselors should uphold for clients facing the end of their life, including the counselor’s role in assisting clients with end-of-life decisions. Counselors were tasked with the responsibility to reflect upon personal values and morals regarding end-of-life to ensure competent and ethical care. Although the revised ACA Code of Ethics (2014) includes considerations for confidentiality, legal concerns, and client safety during end-of-life care, there is no longer a designated section for the end-of-life care of terminally ill clients, and explicit codes regarding PAD are absent. The ACA Code of Ethics (2014) included guidelines for counselors regarding methods to maintain client autonomy and seek continuing education to address the holistic needs of clients, along with giving clients the tools necessary to make the most appropriate decisions for their care. However, lack of explicit codes about PAD and few guidelines related to end-of-life care might cause ambiguity when values-based ethical dilemmas about PAD arise.
In summary, consideration for counselors’ personal and professional values, along with the ethical and legal implications at hand, creates unique potential for a values-based conflict surrounding PAD unlike other sources of values-based conflicts. Values are influenced by numerous factors in multiple settings and contexts (Heller Levitt & Hartwig Moorhead, 2013). Therefore, resolving value conflicts related to PAD warrants a unique systemic perspective that considers the multiple influential sources that shape values about death and grief in personal and professional realms (Neimeyer et al., 2014).
The authors of this article review PAD, counselors’ roles in the hastened death process, and an applied method of managing values-based conflicts with PAD through a values-based ethical decision-making model and ethical bracketing. The impacts of personal and professional values will be described through an ecological systems lens. It is important for counselors to understand PAD in the context of various systems, as individuals’ decisions concerning PAD are influenced by multiple sources that contribute to their beliefs and values related to death and dying.
Physician-Assisted Death
PAD is currently legal in six U.S. states: California, Colorado, Montana (by court ruling), Oregon, Vermont, and Washington, as well as Washington, DC (Death with Dignity, 2018). Hawaii will become the seventh state to legalize PAD when their legal statute takes effect in January 2019 (Death with Dignity, 2018). PAD has been a topic of debate throughout American society and health care for decades (Werth & Holdwick, 2000). Many have voiced opposition to PAD as a legalized option (Werth & Holdwick, 2000), and previous “standards of mental health practice [have treated] all suicides as products of mental illness” (Cohen, 2001, p. 279). However, health care advocates of PAD, such as Dr. Jack Kevorkian, have fought for individual rights to choose dignified death when faced with terminal illness (Kevorkian, 1991). As the legalization of PAD emerged in the aforementioned states, the topic of debate shifted from the right to choose hastened death toward the policies that guide health care professionals to assist terminally ill individuals in hastening their deaths (Werth & Holdwick, 2000).
Language within each state statute slightly varies, but requirements to legally hasten death are similar across states. There are no formal requirements for PAD in Montana, because a law permitting PAD does not exist in that state; however, there is a legal precedent that protects physicians from prosecution as long as there is written consent from the patient (Baxter v. Montana, 2009). For all other states, patients must be over the age of 18, permanent residents of the state, have been determined by an attending and consulting physician to be suffering from a terminal illness, and carry a life expectancy of under 6 months to be eligible to legally hasten their deaths. Patients must voluntarily express their wishes to die orally, make a written request for medication to end their lives in a humane and dignified manner, and be deemed mentally competent to make end-of-life decisions by a licensed psychiatrist or psychologist. In addition, there is typically a 15-day waiting period between the initial request and when the physician provides a written prescription for medication to end life (Death with Dignity, 2018).
In the legal requirements of each state and district statute, there is no mandate for counseling services beyond an assessment of competency. However, PAD clients and their families often work with mental health professionals throughout the process of considering hastened death and implementing PAD (Fulmer, 2014). As more states move toward legislation to legalize PAD, counselors are becoming more involved in the interdisciplinary teams of health professionals working to meet the needs of this population. Interdisciplinary teams may be comprised of medical physicians, psychiatrists, psychologists, social workers, palliative care nurses and specialists, occupational therapists, and mental health counselors (O’Connor & Fisher, 2011). Clients pursuing PAD have physical, social, emotional, spiritual, and practical needs as they deal with the process and experience of dying (Daneker, 2006). Helping professionals’ roles can be blurred as the interdisciplinary team works together to meet PAD clients’ needs (O’Connor & Fisher, 2011). Physical needs include keeping clients comfortable in their final months of life when all other treatment options are exhausted. Practical needs include making arrangements for after death and navigating the legal processes to hasten death, including the competency assessment a psychiatrist or psychologist must conduct to ensure that PAD clients are stable and well-informed enough to decide to hasten their death (O’Connor & Fisher, 2011). Clients’ social, emotional, and spiritual needs will vary depending on the nature of the terminal illness, individual contexts, and familial and cultural contexts; counselors are trained to address such biopsychosocial needs within clients’ individual and cultural contexts (Peruzzi, Canapary, & Bongar, 1996; Werth & Crow, 2009).
A counselor’s primary role is to address how clients’ medical diagnoses are impacting their biopsychosocial well-being, including their decision-making processes to hasten death (O’Connor & Fisher, 2011; Peruzzi et al., 1996; Werth & Crow, 2009). Counselors build a unique therapeutic relationship that provides professional emotional support, and they help clients reflect on the factors that have led them to make this life-ending decision. They may explore what hastened death means to clients’ families or communities. Counselors also seek to understand how clients’ spiritual beliefs and emotional needs influence their well-being and decision making. Counselors recognize that spirituality and religious practices can be significant to clients when discussing dying, death, and grief (Altmaier, 2011). Addressing these factors allows counselors to be intentional in creating a safe setting for difficult discussions.
Standards of Counseling Practice With Dying Clients
The ACA Code of Ethics (2014) not only serves as a guide to ethical practice in counseling, but also provides an understanding of the goals and mission of the counseling profession. Counselors are committed to engaging in “a professional relationship that empowers diverse individuals, families, and groups to accomplish mental health [and] wellness” (ACA, 2014, p. 3). In order to engage in such a relationship with ethical integrity, counselors consider the six principles of ethical behavior: autonomy, nonmaleficence, beneficence, justice, fidelity, and veracity (ACA, 2014). These principles are foundational to the ways in which counselors practice ethically across diverse client groups and settings. Counselors working with PAD clients should review relevant ethical codes concerning end-of-life issues, personal value conflicts, and confidentiality concerns pertinent to fulfilling the needs of terminally ill clients. Of these relevant issues, one specific code includes guidance in managing personal values in counseling:
Counselors are aware of—and avoid imposing—their own values, attitudes, beliefs, and behaviors.
Counselors respect the diversity of clients . . . and seek training in areas in which they are at risk
of imposing their values onto clients, especially when the counselor’s values are inconsistent with
the client’s goals or are discriminatory in nature. (ACA, 2014, A.4.b)
As counselors confront the socioemotional and spiritual needs of PAD clients, regulating personal values related to PAD is of utmost importance for the well-being of a dying client (Werth, 1999).
Values and PAD
Personal values exist at individual, professional, and societal levels. Counselors develop and mold their values in multiple contexts and through various experiences in their lifetime. Thus, counselors’ values surrounding death, dying, and PAD are multifaceted and influenced by multiple factors. Counselors’ views and values surrounding death may be impacted by age, race, gender, religion or spiritual beliefs, phase of life, family structure and influence, cultural identity (e.g., individualistic vs. collectivistic), and education (Bevacqua & Kurpius, 2013; Harrawood, Doughty, & Wilde, 2011; Kemmelmeier, Wieczorkowska, Erb, & Burnstein, 2002). How these factors are interwoven into personal views and values depends on counselors’ perceptions of their experiences and influences from their surrounding environments.
Because personal values are constructed and influenced by a multitude of factors and environments (Heller Levitt & Hartwig Moorhead, 2013), a systemic perspective can be used to appropriately explore and understand how personal values may form and influence counselors. Bronfenbrenner (1979) established the ecological model to describe an individual’s development within four ecosystems: the microsystem, mesosystem, exosystem, and macrosystem. In 1994, Bronfenbrenner revised the ecological model to include the chronosystem, which considers the influence of time and history as individuals develop. Each ecosystem interacts with the others and influences how each ecosystem forms and impacts the developing individual. The ecosystems can be understood as “a set of nested structures, each inside the next, like a set of Russian dolls” (Bronfenbrenner, 1979, p. 3). Next to the chronosystem, the outermost system, the macrosystem encompasses one’s culture, societal norms, and traditions. The exosystem lies within the macrosystem and represents the interactions between environments that may or may not directly affect an individual’s daily interactions. An example of this system would be a parent having trouble at work, and that stressor then affecting the relationship with the child. Within the exosystem is the mesosystem. The mesosystem includes the interactions between the individual’s microsystem and has direct effects on the individual. Lastly, the microsystem involves the individual’s immediate settings and relationships. Relationships can include family and caregivers among others in the environment. Each of these ecosystems and the interactions between them impact the developing individual’s behaviors (Bronfenbrenner, 1979).
Within a systemic ecological perspective, beliefs and values can be viewed as forming and ensuing through layers of influence first from the macrosystem and filtered down through the exosystem, mesosystem, and microsystem (Bronfenbrenner, 1979). The chronosystem includes a history of culture that influences development over time, but the cultural expressions of such influence play out in the macrosystem (Bronfenbrenner, 1994). The macrosystem, the most external of systemic influence, can include societal norms of death and dying and a religious or spiritual belief system. These norms and belief systems influence the exosystem, where laws and regulations exist (e.g., the right for individuals to hasten death in legalized states). Events that occur in the exosystem might not directly include counselors, but they impact the ways in which counselors interact with their lower systems (e.g., news reports of terminally ill patients miraculously overcoming illness).
Through the mesosystem structure, counselors directly engage with multiple settings that influence their beliefs surrounding death and dying (e.g., work and family). Counselors’ interactions with two settings, such as workplace and family, will shed light onto how beliefs, values, and behaviors about death and dying are experienced in each setting. Counselors’ values are subsequently influenced by the interactions between the two settings. Finally, direct experiences in counselors’ immediate settings, the microsystem, impact the unique views and values counselors espouse. Although values filter through larger systems with influence from external factors that impact multiple people, counselors will form distinct perceptions of their experiences that inform their intrapersonal reactions to death and dying (Werth & Crow, 2009).
As counselors consider each layer of the surrounding environment that informs their personal values, they face the values of the counseling profession in the mesosystem. The ACA Code of Ethics (2014) highlighted five fundamental professional values:
- enhancing human development throughout the lifespan;
- honoring diversity and embracing a multicultural approach in support of the worth, dignity,
potential, and uniqueness of people within their social and cultural contexts;
- promoting social justice;
- safeguarding the integrity of the counselor–client relationship; and
- practicing in a competent and ethical manner. (p. 3)
These values provide a foundation for counselors’ ethical behaviors and decisions and inform the collective identity of the counseling profession.
Counselors first encounter professional values in their training programs and are continually exposed to new expressions of professional values throughout their careers. Counselors are nurtured throughout their development to integrate their personal attributes with professional factors as they form an identity congruent with the counseling profession (D. M. Gibson, Dollarhide, & Moss, 2010; Post & Wade, 2009). The ways in which counselors integrate professional values and develop their identities depends on the culture of their training programs, professional work settings, experiences in those settings, and individual perceptions that form from those experiences (Francis & Dugger, 2014). As a result, counselors may vary in their level of support for PAD, personal conflicts related to PAD, and general beliefs and values about death and dying. Therefore, counselors must evaluate their values at a personal and professional level as they work through value conflicts and ethical dilemmas with PAD clients (Johnson, Hayes, & Wade, 2007).
Ethical Decision Making and Bracketing
Counselors’ abilities to resolve value conflicts are determined through ethical decision making (Cottone & Tarvydas, 2016; Kocet & Herlihy, 2014). The ACA Code of Ethics (2014) serves as a guide to counselors to uphold equitable standards of care across client populations when ethical dilemmas and value conflicts arise. According to ACA:
When counselors are faced with ethical dilemmas that are difficult to resolve, they are expected to
engage in a carefully considered ethical decision-making process, consulting available resources as
needed. Counselors acknowledge that resolving ethical issues is a process; ethical reasoning
includes consideration of professional values, professional ethical principles, and ethical
standards. (ACA, 2014, p. 3)
Becoming an ethical decision maker is most effectively done through practice in intentional decision-making processes (P. A. Gibson, 2008). There are many ethical decision-making models that are relevant to maintaining ethical integrity during a variety of dilemmas (Cottone & Tarvydas, 2016). Counselors most often use practice-derived models that are produced from counselors’ experiences and are intended to provide a step-by-step guide for practice (Cottone & Tarvydas, 2016). Although each model is distinct in its step-by-step process, there are common elements throughout them that highlight a standard of practice for ethical decision making. Significant commonalities include gathering information; considering the context of the situation; reviewing codes, standards, and laws; evaluating the counselor’s values or biases; consultation; developing a plan; and executing the plan. For counselors working with PAD clients, their decision-making processes will require a more in-depth exploration of the context of the situation, counselors’ values and biases, and the counseling profession’s values (Heller Levitt & Hartwig Moorhead, 2013; Kurt & Piazza, 2012). Thus, a decision-making model that carefully considers values-based conflicts is needed.
Using a practice-derived framework, Kocet and Herlihy (2014) developed the counselor values-based conflict model (CVCM) to specifically address ethical dilemmas stemming from value conflicts. The model includes five steps: (1) determine nature of values-based conflict (personal or professional); (2) explore core issues and potential barriers to providing appropriate standard of care; (3) seek assistance/remediation for providing appropriate standard of care; (4) determine and evaluate possible courses of action; and (5) ensure that proposed actions promote client welfare (Kocet & Herlihy, 2014). Each step includes consideration for potential personal and professional values that may arise for counselors.
A key part of resolving values-based conflicts is avoiding imposing one’s values onto the client. To address this key issue, Kocet and Herlihy (2014) also introduced the term ethical bracketing. Ethical bracketing in qualitative research is “a reflexive process [that] enables [researchers] to bracket or set aside their own experiences and assumptions when they interact with their participants and thus accurately capture their participants’ voices” (Kocet & Herlihy, 2014, p. 182). To apply this concept to counseling, Kocet and Herlihy stated that ethical bracketing
is defined as the intentional separating of a counselor’s personal values from his or her
professional values or the intentional setting aside of the counselor’s personal values in order to
provide ethical and appropriate counseling to all clients, especially those whose worldviews,
values, belief systems, and decisions differ significantly from those of the counselor. (p. 182)
Counselors can engage in ethical bracketing by seeking supervision, consultation, continuing education, and personal counseling (Kocet & Herlihy, 2014). This bracketing technique allows counselors to confront their values and establish awareness of how their values may be impacting their views and interactions with clients. Counselors may more easily recognize the unique worldviews of clients through this process, thereby respecting the diversity of clients in their cultural contexts. Such recognition protects the welfare of clients as counselors strive to work from the client’s worldview rather than their own (ACA, 2014). The CVCM, along with ethical bracketing, can be used as a guiding ethical decision-making framework for counselors to explore the systemic nature of their values and resolve values-based conflicts with PAD.
Values-Based Ethical Decisions and Bracketing With PAD
The CVCM is designed to assist counselors in managing personal conflicts related to values that may arise when working with clients (Kocet & Herlihy, 2014). The model begins with a prompt for counselors to determine if the nature of the conflict is personal or professional and ensues with steps that align with the nature of the conflict. However, considering the systemic makeup of individual values, particularly related to PAD, counselors must be mindful of the influences that stem from the profession’s values in the formation and modification of their personal values. Personal and professional values are interwoven and will consequently impact the ethical decision-making process related to values-based conflicts with PAD (Heller Levitt & Hartwig Moorhead, 2013). As a result, adding a systemic lens to the process of resolving values-based conflicts using the CVCM and ethical bracketing is important to maintaining ethical practice with PAD clients.
The systemic sources of values related to PAD are important to consider in the second step of the CVCM; this step includes a prompt for counselors to “explore core issues and potential barriers to providing appropriate standard[s] of care” (Kocet & Herlihy, 2014, p. 184). Gathering awareness about counselors’ personal views related to death, dying, and PAD is the crux of working through this step in the model. As previously discussed, counselors must engage in reflective practice to examine influential factors throughout each ecosystem. Each system contributes to counselors’ personal views and beliefs, and reflecting will bring awareness to not only the sources of counselors’ values, but also potential barriers to overcoming values-based conflicts (Bronfenbrenner, 1979; Cottone & Tarvydas, 2016; Kocet & Herlihy, 2014).
Beginning with the macrosystem, societal norms and religious and spiritual views of death and dying will influence the exosystem. Legislation that gives clients legal freedom in certain states to decide to end their lives is situated in the exosystem. As the decision to engage in PAD is legalized, it then trickles down into the mesosystem where groups, such as work colleagues and family, hold beliefs and values about PAD. These beliefs and values influence counselors in new ways and impact the intrapersonal reactions counselors have in their microsystem of experience. Counselors must examine the interactions between settings and the messages they receive in those settings. Then, they may more readily discover how their values and beliefs about PAD are formed and either reinforced or undermined. Increased awareness will help counselors identify the ecosystem that is the most salient source of their value conflict with PAD (Bronfenbrenner, 1979). Identifying the salient source may then lead to increased potential for counselors to be more specific in the ways they strategize to bracket their values.
As counselors foster awareness about the sources of their value conflicts, they can move into the third step and engage in ethical bracketing as a strategy to seek necessary assistance to resolve value conflicts. In addition to referring to the ACA Code of Ethics (2014), counselors may consult with other counselors to explore individualized strategies to engage with PAD clients without imposing personal beliefs and value systems. Consultation with other professionals will shed light onto professional standards of care for PAD clients, while also serving as a mirror for further self-exploration about the sources and nature of value conflicts with PAD. It is important to note that counselors should “identify ways to maintain personal/religious/moral beliefs while still providing effective counseling” (Kocet & Herlihy, 2014, p. 184). Ethical bracketing is not designed to push counselors to give up their beliefs or values; rather, counselors simply “set aside their own experiences and assumptions” to effectively step into the client’s worldview (Kocet & Herlihy, 2014, p. 182). Seeking supervision, consultation, and personal counseling can provide guidance for counselors to determine their needs to maintain their personal beliefs and deliver ethical care for PAD clients (Cottone & Tarvydas, 2016; Kocet & Herlihy, 2014).
Next, counselors shift into the fourth step to “determine and evaluate possible courses of action” (Kocet & Herlihy, 2014, p. 184). Using ethical bracketing as a strategy may provide distinct options to consider in this step. Once counselors are aware of the intricacies of their values-based conflict with PAD, they may be more readily able to bracket their values. The guidelines for use of the CVCM in the fourth step note client referral; however, counselors may only refer when they “lack the competence to be of professional assistance to clients,” and their rationale is not the result of personal bias (ACA, 2014, A.11.a.). If counselors lack competence, they may seek appropriate continuing education and supervision to expand their competency in the future. However, in the case of personal value conflicts, referral is not ethical. There is no statement in the ACA Code of Ethics (2014) “that [indicates] referral can be made on the basis of counselor values” (Kaplan, 2014, p. 144). Self-evaluation and consultation is essential to maintain ethical practice surrounding this topic. Once a course of action has been determined as ethical and effective, counselors engage in the fifth step to “ensure that proposed actions promote client welfare” (Kocet & Herlihy, 2014, p. 184). In order to more fully conceptualize resolving values-based conflicts with PAD through this model, a specific example is provided in the following section.
Case Study Application
The following case study explores a counselor’s values-based conflict related to PAD for illustrative purposes. Although many sources may contribute to potential values-based conflicts, personally held religious beliefs are often influential to views and values about PAD (Bevacqua & Kurpius, 2013; Burdette, Hill, & Moulton, 2005; Reiner, 2007). Therefore, personal religious beliefs are explored for the purposes of this case study. Considering a systemic view of counselors’ values, the CVCM and ethical bracketing are used to generate potential conflict resolutions that ensure ethical practice and protect the welfare of the client.
Vignette
Amy is a licensed professional counselor in the state of Washington. She works for an agency that receives referrals from a local hospital. Amy identifies as a religious person and has connections and support through her religious community. Her personal religious views do not endorse hastening one’s death, even under extreme circumstances like a terminal illness. Amy also has two young children.
Amy has been meeting with Frankie, a 40-year-old woman, for about four months. Frankie was diagnosed with leukemia about six months ago and began treatment shortly thereafter. Frankie recently found out that the leukemia is not responding to treatment and her treatment options are exhausted. Frankie’s oncologist has estimated a five- to six-month life expectancy. Frankie has expressed to Amy that she wants to pursue PAD so that she does not have to be in pain for 6 more months. Frankie has a husband and 6-year-old daughter.
Amy is initially shocked to hear Frankie’s desire to hasten her death. Amy is unsure how to proceed in her work with Frankie because she feels Frankie’s decision conflicts with her religious beliefs. Amy also is wondering if Frankie has considered how her family feels and if they would be okay with Frankie’s decision. Recognizing she needs to process her thoughts and feelings, Amy seeks out a helpful colleague in order to proceed in her work with Frankie.
Discussion
Beginning with the first step of the CVCM, Amy appears to be dealing with a complex values-based conflict. The nature of Amy’s conflict is primarily personal, but she is faced with some professional conflicts as well. Amy’s religious beliefs and values are personally driven, but the countertransference she is experiencing related to Frankie’s seeming lack of concern for her family can become a professional issue if Amy considers making professional decisions that emphasize family values over Frankie’s requests (Heller Levitt & Hartwig Moorhead, 2013). Furthermore, Amy’s personal religiously driven value conflict intertwines with the counseling profession’s value and ethical standard to respect clients’ worldviews and not impose personal beliefs onto clients (ACA, 2014, A.4.b). Understanding both personal and professional implications allows counselors to move into the second step of the CVCM.
The development and context of Amy’s values may be explored through a systemic ecological lens in the second step. Beginning with the macrosystem, Amy may consider how her religious culture views death and what messages she has internalized to form her understanding of morality and autonomy (Burdette et al., 2005; Johnson et al., 2007). She also could explore how society at large influences her religious beliefs and practices and subsequently how she believes her religion views the practice of hastened death. The interaction between Amy’s religious culture and society is situated in the exosystem. Amy’s interactions with her religious community, which are a part of her mesosystem, also will play a role in her beliefs and actions. She might think about how her immediate community impacts her beliefs and influences her perceptions of hastened death; Amy’s individual perceptions and direct engagement with her religious practices play out in her microsystem. As each ecosystem is explored, Amy can develop a clear understanding of the sources of her value conflict. The same process should be repeated for her values-based conflict about Frankie’s family. Amy may value collective family decisions and could potentially struggle to meet Frankie with acceptance if she believes an isolated decision is improper.
Once Amy has explored the systemic sources of her values, she is ready to seek assistance to ethically move forward with Frankie in the third step of the CVCM. Using ethical bracketing, Amy can reach out to her colleagues to consult about the issues at hand. Exploring her values with a trusted professional may enable her to bracket her values to approach Frankie’s differing beliefs and values. Amy must review the ACA Code of Ethics (2014) before creating a plan of action. Again, Code A.4.b, regarding personal values and biases, is central to an ethical course of action; the profession’s value of client autonomy and Code A.1.a, to protect the welfare of the client, also are important to consider here (ACA, 2014). Attending to legal implications, Amy should keep in mind that Frankie has a legal right in the state of Washington to decide to hasten her death. Lastly, Amy should consider ways she can maintain her own values without compromise while still providing effective care and assistance to Frankie in her decision-making process (Kocet & Herlihy, 2014). Amy may pursue personal counseling or supervision and connect with trusted individuals in her religious community to maintain her personal beliefs and values while providing ethical care (Cottone & Tarvydas, 2016; Johnson et al., 2007).
Moving into the fourth step of the CVCM, referral is an option only if Amy lacks competence to provide Frankie with effective care. According to the CVCM, when a counselor is determining action plans, the choice to refer a client is decided after careful consideration of ethical guidelines, rationale for the referral, and in-depth consultation (Kocet & Herlihy, 2014). Referral based on personal values is not ethical according to the ACA Code of Ethics (2014); therefore, Amy cannot ethically refer Frankie, considering the source of her conflict is related to personal values.
Finally, in the fifth step, Amy can ensure her constructed course of action considers both legal and ethical implications. The rationale for Amy’s action plan should be based on professional competency, not personal bias (ACA, 2014, A.11.a). Amy’s ability to effectively bracket her values will be dependent on her depth of self-exploration, understanding of ethical practice in counseling, willingness to consult and seek appropriate resources, and ability to ensure client welfare as the priority. It is essential for Amy to seek consultation from her professional peers, who can provide insight into maintaining ethical boundaries with clients. Also, Amy can receive permission to speak with Frankie’s lawyer and the primary doctors involved with her decision to hasten her death. By increasing involvement with Frankie’s interdisciplinary team, Amy is ensuring holistic care and attending to the systemic nature of end-of-life decision making surrounding PAD.
Implications for Counseling Practice
The interplay between PAD and the values of counselors and the counseling profession is complex and warrants depth of exploration for counselors to effectively meet the needs of this population. Values-based conflicts do not occur in isolation; instead, multiple systems that impact individuals in varying ways influence the formation and expression of such conflicts (Heller Levitt & Hartwig Moorhead, 2013). No one specific cultural identity, belief, or value can predict a counselor’s conflicts with PAD, but it is crucial to explore values through a systemic lens to successfully manage values-based conflicts with PAD. The CVCM, along with ethical bracketing, can serve as an appropriate framework to confront and resolve values-based conflicts with PAD. Counselors will be better equipped to provide care to PAD clients as they willingly and openly explore their values related to death, dying, and hastening death through an ethical decision-making model (ACA, 2014). Counselors’ effectiveness in self-reflection and ethical practice is reliant in part on counselor education.
Counselor Education
As state laws change, counselor educators need to recognize that counselors will play a larger role in caring for potential PAD clients. It can be beneficial to learn about the role of value bracketing in regard to discussing the possibility of a client exploring the option of PAD. It is difficult for counselor educators to prepare counselors-in-training (CITs) for every potential ethical dilemma. However, with a better understanding of PAD, novice counselors can feel more equipped to effectively address concerns their clients may have without interference of their personal beliefs and values. PAD is a topic that will continue to expand. Introducing PAD during training may allow counselors to feel more prepared should a value conflict arise. As counselor educators facilitate conversations with CITs about their personal and professional beliefs toward PAD, CITs can implement their value bracketing skills under the supervision of their faculty. Being in a safe environment can encourage CITs to explore their authentic feelings concerning PAD and evaluate their value bracketing skillset. Addressing concerns and potential red flags during training can prevent harm to future clients and unethical clinical judgment and behaviors.
There is a potential challenge in maintaining consistency in training about end-of-life issues, including PAD, because of the nature of accreditation standards for counseling programs. There is no specific standard of learning in the 2016 Council for Accreditation of Counseling and Related Educational Programs (CACREP) standards regarding end-of-life counseling issues (CACREP, 2016). Counselor educators are tasked to meet learning standards related to human growth and development “across the lifespan,” but they have discretion over what they include and highlight throughout their curriculum (CACREP, 2016, p. 10). Counselor educators should consider the importance and advantages of including specific instruction on end-of-life issues in their curriculum (Servaty-Seib & Tedrick Parikh, 2014).
In addition to educating CITs, more research is needed to further understand counselors’ developing roles with clients pursuing PAD. With more states legalizing this procedure, it is only a matter of time before counselors are face-to-face with a client that needs a counselor’s experience and competency to assist with this life-changing decision. Although data is available concerning grief and loss counseling, literature directly related to counselors’ roles in working with PAD is sparse. Future research should incorporate counselors’ emerging roles with PAD clients and needs for training to prepare CITs. With stronger research in this area, counselor educators may feel more equipped to teach and support CITs
to become aware of and potentially bracket their values about death, dying, and PAD.
Conclusion
Counselors must be knowledgeable about the legal and ethical standards surrounding PAD in order to work effectively and ethically with PAD clients. Counselors also need to be aware of their personal beliefs and values about death and dying and be able to manage values-based conflicts. This article highlighted personal and professional values relevant to counselors working with PAD clients through an ecological systems lens. Considering the values at play, counselors can use the CVCM with ethical bracketing as an integrated method to resolve value conflicts with PAD (Kocet & Herlihy, 2014). Increased knowledge regarding ethical decision making surrounding PAD can encourage counselors to provide care for PAD clients with competence and confidence. Further research on counselors’ roles with PAD clients and needs for training may enhance counselors’ knowledge and competency with this client population.
Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest or funding contributions for the development of this manuscript.
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Nancy E. Thacker, NCC, is a doctoral candidate at the University of Tennessee, Knoxville. Jillian M. Blueford, NCC, is a doctoral candidate at the University of Tennessee, Knoxville. Correspondence can be addressed to Nancy Thacker, 501 BEC, 1122 Volunteer Blvd, Knoxville, TN 37996-3452, nthacke2@vols.utk.edu.
Sep 27, 2018 | Volume 8 - Issue 3
Christopher T. Belser, M. Ann Shillingford, Andrew P. Daire, Diandra J. Prescod, Melissa A. Dagley
The United States is facing a crisis with respect to filling job vacancies within science, technology, engineering, and math (STEM) industries and with students completing STEM undergraduate degrees. In addition, disparities exist for females and ethnic minorities within STEM fields. Whereas prior research has centered on disparities in STEM fields, retention rates, and some intervention programs, researchers have not given much attention to the role of career development initiatives within STEM recruitment and retention programming. The purpose of the present study was to incorporate demographic variables, math performance, and career development–related factors into predictive models of STEM retention with a sample of undergraduate students within a STEM recruitment and retention program. The resulting two models accurately predicted first-year to second-year retention with 73.4% of the cases and accurately predicted first-year to third-year retention with 70.0% of the cases. Based on the results, the researchers provide a rationale for STEM career programming in K–12 and higher education settings and for the inclusion of career development and career counseling in STEM education programming.
Keywords: STEM, retention, career development, career counseling, undergraduate student
The United States lacks an adequate number of workers to keep up with the demand for trained workers in science, technology, engineering, and mathematics (STEM) fields (National Center for Science and Engineering Statistics [NCSES], 2017; National Science Board, 2018; Sithole et al., 2017). Researchers have pointed to the overall stagnancy of undergraduate students declaring and completing STEM degrees (Carnevale, Smith, & Melton, 2011; Doerschuk et al., 2016; Sithole et al., 2017). Additionally, underrepresentation is a problem for racial and ethnic minorities and females in STEM fields (NCSES, 2017). Because of these disparities, universities have developed programs centered on recruitment and retention of STEM undergraduates (Bouwma-Gearhart, Perry, & Presley, 2014; Dagley et al., 2016; Schneider, Bickel, & Morrison-Shetlar, 2015) and both government and private entities invest billions of dollars annually toward STEM initiatives at the K–12 and higher education levels (Carnevale et al., 2011). However, many of these endeavors have failed to incorporate components centered on career development or career planning.
The National Career Development Association (2015) defined career development as “the sequence of career-related choices and transitions made over the life span” (p. 4) and career planning as a structured process through which a person makes decisions and plans for a future career. Career development activities, such as structured career planning courses, have shown efficacy with general undergraduate populations (Osborn, Howard, & Leierer, 2007; Reardon, Melvin, McClain, Peterson, & Bowman, 2015) but have been studied less commonly with STEM-specific undergraduate populations (Belser, Prescod, Daire, Dagley, & Young, 2017, 2018; Prescod, Daire, Young, Dagley, & Georgiopoulos, in press). In the present study, researchers examined a STEM recruitment and retention program that did include a career planning course. More specifically, the research team sought to investigate relationships between demographics (e.g., gender, ethnicity), math scores, and various aspects of the undergraduate STEM program and student retention in the first 2 years of college.
Gender, Ethnicity, and STEM
Gender disparities are a common sight within STEM degree programs and the larger STEM workforce (NCSES, 2017). Females who are interested in math and science are more likely to be tracked into non-diagnosing health practitioner fields, such as nursing (ACT, 2018; NCSES, 2017). Some researchers have pointed to the K–12 arena as the root of these gender disparities that permeate undergraduate programs and STEM professions (Mansfield, Welton, & Grogan, 2014), whereas others have identified specific problems, such as differences in math and science course completion over time (Chen & Soldner, 2013; Riegle-Crumb, King, Grodsky, & Muller, 2012), stereotype threat (Beasley & Fischer, 2012), and STEM confidence (Litzler, Samuelson, & Lorah, 2014). As a result, existing predictive models typically indicate a lower likelihood of females completing a STEM degree compared to male students (Cundiff, Vescio, Loken, & Lo, 2013; Gayles & Ampaw, 2014).
Similarly, disparities in STEM degree completion and STEM job attainment exist between ethnic groups (NCSES, 2017; Palmer, Maramba, & Dancy, 2011). Although progress has been made in degree attainment in certain STEM areas, other areas have stagnated or are declining in participation by ethnic minority students (Chen & Soldner, 2013; NCSES, 2017). Foltz, Gannon, and Kirschmann (2014) identified protective factors for minority students in STEM, such as receiving college-going expectations from home, establishing connections with STEM faculty members (particularly those of color), and developing connections with other minority students in STEM majors; however, the disparities in STEM programs help perpetuate a cycle of many students not being exposed to these protective factors. The intersectionality of ethnicity and gender in STEM fields has become a topic producing interesting findings (Riegle-Crumb & King, 2010). In addition to observing disparities across ethnic groups, researchers have observed disparities within ethnic groups based on gender (Beasley & Fischer, 2012; Cundiff et al., 2013; Riegle-Crumb & King, 2010). Specifically with males of color, predictive models have been inconclusive, with some showing a higher likelihood of completing a STEM degree (Riegle-Crumb & King, 2010) and others showing a lower likelihood (Cundiff et al., 2013; Gayles & Ampaw, 2014).
Mathematics and STEM
The SAT is one of the most widely used college admissions tests (CollegeBoard, 2018). Researchers have correlated the math sub-score with undergraduate math and science classes within the first year, indicating that higher SAT math scores indicate a higher probability of higher course grades in math and science courses (Wyatt, Remigio, & Camara, 2012). Additionally, researchers have identified SAT scores as predictors of academic success and university retention (Crisp, Nora, & Taggart, 2009; Le, Robbins, & Westrick, 2014; Mattern & Patterson, 2013; Rohr, 2012). Despite its wide use in higher education admissions, the SAT may not be free from bias. Numerous scholars have highlighted potential test bias, particularly against ethnic minorities (Dixon-Román, Everson, & McArdle, 2013; Lawlor, Richman, & Richman, 1997; Toldson & McGee, 2014). Nevertheless, its wide use makes it a prime instrument for research.
In addition to the SAT scores, researchers also have demonstrated that taking higher-level math courses and having higher math self-efficacy translate to better outcomes within STEM majors (Carnevale et al., 2011; Chen & Soldner, 2013; Nosek & Smyth, 2011). Specifically, taking calculus-based courses in high school correlated with retention in STEM majors (Chen & Soldner, 2013). Nosek and Smyth (2011) found connections between gender and internalized math variables, such as warmth for math, identification with math, and self-efficacy; females across the life span showed lower levels of each of these variables, but the authors did not test these against retention outcomes in STEM majors. However, one could hypothesize that having lower levels of warmth toward math and not being able to identify with math would likely impact one’s career decisions, particularly related to math and science fields.
Career Interventions and STEM
Career theory can provide for understanding one’s interest in STEM fields (Holland, 1973), one’s exposure to STEM fields (Gottfredson, 1981), and one’s beliefs or expectations about the process of choosing a STEM field (Lent, Brown, & Hackett, 2002; Peterson, Sampson, Lenz, & Reardon, 2002). However, career interventions, such as a career planning class, are more likely to make a direct impact on career outcomes with undergraduates. In one review of research on undergraduate career planning courses, more than 90% of the courses produced some measurable positive result for students, such as increased likelihood of completing a major, decreased negative career thinking, and increased career self-efficacy (Reardon & Fiore, 2014). Other researchers have reported similar results with generic undergraduate career planning courses (Osborn et al., 2007; Saunders, Peterson, Sampson, & Reardon, 2000).
Researchers have studied structured career planning courses specific to STEM majors with much less frequency. In one such study, Prescod and colleagues (in press) found that students who took a STEM-focused career planning course scored lower on a measure of negative career thinking at the end of the semester. In a similar study, STEM-interested students in a STEM-focused career planning course had lower posttest scores on a measure of negative career thinking than declared STEM majors at the end of the same semester (Belser et al., 2018). Additionally, in a pilot study, Belser and colleagues (2017) found that greater reductions in negative career thinking predicted higher odds of being retained in a STEM major from the first to second year of college; in this same study, the authors found that students who participated in a STEM-focused career planning course were more likely to be retained in a STEM major than students in an alternative STEM course. Researchers have not given ample attention to determining how career planning and other career variables fit into predictive models of retention in STEM majors.
Statement of the Problem and Hypotheses
As previously noted, prior researchers have paid limited attention to developing predictive models that incorporate career development variables along with demographics and math performance. Developing effective predictive models has implications for researchers, career practitioners, higher education professionals, and the STEM workforce. To this end, the researchers intend to test two such models related to retention in STEM majors using the following hypotheses:
Hypothesis 1: First-year to second-year undergraduate retention in STEM majors can be predicted by ethnicity, gender, initial major, math placement–algebra scores, SAT math scores, STEM course participation, and Career Thoughts Inventory (CTI) change scores.
Hypothesis 2: First-year to third-year undergraduate retention in STEM majors can be predicted by ethnicity, gender, initial major, math placement–algebra scores, SAT math scores, STEM course participation, and CTI change scores.
Methods
In this study, researchers examined multi-year retention data for students in a STEM recruitment and retention program at a large research university in the Southeastern United States and utilized a quasi-experimental design with non-equivalent comparison groups (Campbell & Stanley, 1963; Gall, Gall, & Borg, 2007). Because this study was part of a larger research project, Institutional Review Board approval was already in place.
The COMPASS Program
The COMPASS Program (Convincing Outstanding Math-Potential Admits to Succeed in STEM; Dagley et al., 2016) is a National Science Foundation–funded project that seeks to recruit and retain undergraduate students in STEM majors. To enter the program, students must have a minimum SAT math score of 550, an undeclared major at the time of applying to the university and program, and an expressed interest in potentially pursuing a STEM degree. However, some students accepted to the COMPASS Program declare a STEM major between the time that they are accepted into the COMPASS Program and the first day of class, creating a second track of students who were initially uncommitted to a major at the time of application. Students in both tracks have access to math and science tutoring in a program-specific center on campus, are matched with undergraduate mentors from STEM majors, have access to cohort math classes for students within the program, and can choose to live in a residence hall area designated for COMPASS participants. Depending on which COMPASS track students are in, they either take a STEM-focused career planning course or a STEM seminar course during their first semester.
COMPASS participants who started college without a declared major take a STEM-focused career planning class in their first semester. The activities of this course include a battery of career assessments and opportunities to hear career presentations from STEM professionals, visit STEM research labs, and attend structured career planning activities (e.g., developing a career action plan, résumé and cover letter writing, small group discussions). The first author and fourth author served as instructors for this course, and both were counselor education doctoral students at the time.
Participants who had declared a STEM major between the time they were accepted into the COMPASS Program and the first day of class took a STEM seminar course instead of the career planning class. The structure of this course included activities designed to help students engage with and be successful in their selected STEM majors, including presentations on learning styles and strategies, time management, study skills, professional experiences appropriate for STEM majors, and strategies for engaging in undergraduate research. Guest speakers for the class focused more on providing students with information about how to be successful as a STEM student. The course did not include career planning or career decision-making activities specifically geared toward helping students decide on a major or career field. A science education doctoral student served as the instructor of record for the course, with graduate students from various STEM fields serving as teaching assistants.
Participants
The university’s Institutional Knowledge Management Office provided demographic data on program participants. Table 1 displays descriptive data for participants, organized by second-year retention data (i.e., retention from the first year of college to the second year of college, for Hypothesis 1) and third-year retention data (i.e., retention from the first year of college to the third year of college, for Hypothesis 2). The frequencies for the subcategories were smaller for the third-year retention data (Hypothesis 2) because fewer participants had matriculated this far during the life of the project. Table 1 also breaks down each subset of the data based on which students were retained in a STEM major and which were not retained.
Table 1
Descriptive Statistics for Categorical Variables
|
Second-Year Retention Descriptives |
Third-Year Retention Descriptives |
| Variables |
Retained |
Not Retained |
Total |
Retained |
Not Retained |
Total |
|
n |
%a |
n |
%b |
n |
%c |
n |
%a |
n |
%b |
n |
%c |
| Gender |
|
|
|
|
|
|
|
|
|
|
|
|
| Male |
159 |
58.9 |
74 |
46.5 |
233 |
54.3 |
72 |
55.8 |
65 |
44.8 |
137 |
50.0 |
| Female |
111 |
41.1 |
85 |
53.5 |
196 |
45.7 |
57 |
44.2 |
80 |
55.2 |
137 |
50.0 |
| Total |
270 |
100.0 |
159 |
100.0 |
429 |
100.0 |
129 |
100.0 |
145 |
100.0 |
274 |
100.0 |
| Ethnicity |
|
|
|
|
|
|
|
|
|
|
|
|
| Caucasian/White |
147 |
54.4 |
100 |
62.9 |
247 |
57.6 |
66 |
51.2 |
85 |
58.6 |
151 |
55.1 |
| African Am./Black |
31 |
11.5 |
16 |
10.1 |
47 |
11.0 |
16 |
12.4 |
18 |
12.4 |
34 |
12.4 |
| Hispanic |
57 |
21.1 |
34 |
21.4 |
91 |
21.2 |
29 |
22.5 |
32 |
22.1 |
61 |
22.3 |
| Asian/Pacific Islander |
24 |
8.9 |
4 |
2.5 |
28 |
6.5 |
10 |
7.8 |
5 |
3.4 |
15 |
5.5 |
| Other |
11 |
4.1 |
5 |
3.1 |
16 |
3.7 |
8 |
6.2 |
5 |
3.4 |
13 |
4.7 |
| Total |
270 |
100.0 |
159 |
100.0 |
429 |
100.0 |
129 |
100.0 |
145 |
100.0 |
274 |
100.0 |
| Course |
|
|
|
|
|
|
|
|
|
|
|
|
| Career Planning |
137 |
50.7 |
120 |
75.5 |
257 |
59.9 |
76 |
58.9 |
112 |
77.2 |
188 |
68.6 |
| STEM Seminar |
133 |
49.3 |
39 |
24.5 |
172 |
40.1 |
53 |
41.1 |
33 |
22.8 |
86 |
31.4 |
| Total |
270 |
100.0 |
159 |
100.0 |
429 |
100.0 |
129 |
100.0 |
145 |
100.0 |
274 |
100.0 |
| Initial Major |
|
|
|
|
|
|
|
|
|
|
|
|
| Undeclared |
130 |
48.1 |
72 |
45.3 |
202 |
47.1 |
65 |
50.4 |
63 |
43.4 |
128 |
46.7 |
| STEM |
124 |
45.9 |
40 |
25.2 |
164 |
38.2 |
55 |
42.6 |
39 |
26.9 |
94 |
34.3 |
| Non-STEM |
16 |
5.9 |
47 |
29.6 |
63 |
14.7 |
9 |
7.0 |
43 |
29.7 |
52 |
19.0 |
| Total |
270 |
100.0 |
159 |
100.0 |
429 |
100.0 |
129 |
100.0 |
145 |
100.0 |
274 |
100.0 |
Note. a = percentage of the Retained group. b = percentage of the Not Retained group. c = percentage of the Total group.
Gender representation within the two samples was split relatively evenly, with female participants represented at a higher rate in the sample than in the larger population of STEM undergraduates and at a higher rate than STEM professionals in the workforce. Both samples were predominantly Caucasian/White, with no other ethnic group making up more than one-fourth of either sample individually; these ethnicity breakdowns were reflective of the university’s undergraduate population and somewhat reflective of STEM disciplines. The students who took the STEM-focused career planning course accounted for a larger percentage of both total samples and also of the not-retained groups. Regarding initial major, the largest percentage of students fell within the initially undeclared category, with the next largest group being the initially STEM-declared group (these students officially declared a STEM major but were uncommitted with their decision).
The researchers conducted an a priori power analysis using G*Power 3 (Cohen, 1992; Faul, Erdfelder, Lang, & Buchner, 2007), and the overall samples of 429 and 271 were sufficient for the binary logistic regression. With logistic regression, the ratio of cases in each of the dependent outcomes (retained or not retained) to the number of independent variable predictors must be sufficient (Agresti, 2013; Hosmer, Lemeshow, & Sturdivant, 2013; Tabachnick & Fidell, 2013). Following Peduzzi, Concato, Kemper, Holford, and Feinstein’s (1996) rule of 10 cases per outcome per predictor, the samples were sufficient for all independent variables except ethnicity, which had multiple categories with fewer than 10 cases. However, Field (2009) and Vittinghoff and McCulloch (2006) recommended having a minimum of five cases per outcome per predictor, which the sample achieved for all independent variables.
Variables and Instruments
The analysis included 10 independent variables within the logistic regression models. The university’s Institutional Knowledge Management Office (IKMO) provided data for the four categorical variables displayed in Table 1 (gender, ethnicity, course, and initial major). Four of the independent variables represented the participants’ total and subscale scores on the CTI, which students completed in either the career planning course or the STEM seminar course. The other two independent variables were participants’ scores on the SAT math subtest and the university’s Math Placement Test–Algebra subscale; the IKMO provided these data as well.
Career Thoughts Inventory (CTI). The CTI includes 48 Likert-type items and seeks to measure respondents’ levels of negative career thinking (Sampson, Peterson, Lenz, Reardon, & Saunders, 1996a, 1996b). To complete the CTI, respondents read the 48 statements about careers and indicate how much they agree using a 4-point scale (strongly disagree to strongly agree). The CTI provides a total score and scores for three subscales: (a) Decision Making Confusion (DMC); (b) Commitment Anxiety (CA); and (c) External Conflict (EC). Completing the instrument yields raw scores for the assessment total and each of the three subscales, and a conversion table printed on the test booklet allows respondents to convert raw scores to T scores. Higher raw scores and T scores indicate a higher level of problematic thinking in each respective area, with T scores at or above 50 indicating clinical significance. For the college student norm group, internal consistency alpha coefficients were .96 for the total score and ranged from .77 to .94 for the three subscales (Sampson et al., 1996a, 1996b). With the sample in the present study, the researchers found acceptable alpha coefficients that were comparable to the norm group. The researchers used CTI change scores as predictors, calculated as the change in CTI total and subscale scores from the beginning to the end of either the career planning class or the STEM seminar class.
SAT Math. High school students take the SAT as a college admissions test typically in their junior and/or senior years (CollegeBoard, 2018). Although the SAT has four subtests, the researchers only used the math subtest in the present study. The math subtest is comprised of 54 questions or tasks in the areas of basic mathematics knowledge, advanced mathematics knowledge, managing complexity, and modeling and insight (CollegeBoard, 2018; Ewing, Huff, Andrews, & King, 2005). In a validation study of the SAT, Ewing et al. (2005) found an internal consistency alpha coefficient of .92 for the math subtest and alpha coefficients ranging from .68 to .81 for the four math skill areas. The researchers were unable to analyze psychometric properties of the SAT math test with the study sample because the university’s IKMO only provided composite and subtest total scores, rather than individual item responses.
Math Placement Test–Algebra Subtest. The Math Placement Test is a university-made assessment designed to measure mathematic competence in algebra, trigonometry, and pre-calculus that helps the university place students in their first math course at the university. All first-time undergraduate students at the university are required to take the test; when data collection began, the mandatory completion policy was not yet in place, so some earlier participants had missing data in this area. The test is structured so that all respondents first take the algebra subtest and if they achieve 70% accuracy, they move to the trigonometry and pre-calculus subtests. Similar to the SAT, the researchers were unable to analyze psychometric properties of the test because the IKMO provided only composite and subtest total scores.
Procedure
Because the dependent variables (second-year retention and third-year retention) were dichotomous (i.e., retained or not retained), the researchers used the binary logistic regression procedure within SPSS Version 24 to analyze the data (Agresti, 2013; Hosmer et al., 2013; Tabachnick & Fidell, 2013). The purpose of binary logistic regression is to test predictors of the binary outcome by comparing the observed outcomes and the predicted outcomes first without any predictors and then with the chosen predictors (Hosmer et al., 2013). The researchers used a backward stepwise Wald approach, which enters all predictors into the model and removes the least significant predictors one by one until all of the remaining predictors fall within a specific p value range (Tabachnick & Fidell, 2013). The researchers chose to set the range as p ≤ .20 based on the recommendation of Hosmer et al. (2013).
Preliminary data analysis included identifying both univariate and multivariate outliers, which were removed from the data file; conducting a missing data analysis; and testing the statistical assumptions for logistic regression. There were no missing values for categorical variables, but the assessment variables (CTI, SAT, and Math Placement Test) did have missing values. Results from Little’s (1988) MCAR test in SPSS showed that these data were not missing completely at random (Chi-square = 839.606, df = 161, p < .001). The researchers chose to impute missing values using the Expectation Maximization procedure in SPSS (Dempster, Laird, & Rubin, 1977; Little & Rubin, 2002). The data met the statistical assumptions of binary logistic regression related to multicollinearity and linearity in the logit (Tabachnick & Fidell, 2013). As previously discussed, the data also sufficiently met the assumption regarding the ratio of cases to predictor variables, with the exception of the ethnicity variable; after removing outliers, the Asian/Pacific Islander subcategory in the non-retained outcome had only four cases, violating the Peduzzi et al. (1996) and Field (2009) recommendation of having at least five cases. However, because the goal was to test the ethnicity categories separately rather than collapsing them to fit the recommendation, and because Hosmer et al. (2013) noted this was a recommendation and not a rule, the researchers chose to keep the existing categories, noting the potential limitation when interpreting this variable.
Results
The sections that follow provide the results from each of the hypotheses and interpretation of the findings.
Hypothesis 1
Hypothesis 1 stated that the independent variables could predict undergraduate STEM retention from Year 1 to Year 2. As stated previously, the backward stepwise Wald approach involved including all predictors initially and then removing predictors one by one based on p value until all remaining predictors fell within the p ≤ .20 range. This process took five steps, resulting in the removal of four variables with p values greater than .20: (a) CTI Commitment Anxiety Change, (b) CTI External Conflict Change, (c) Gender, and (d) CTI Decision Making Confusion Change, respectively. The model yielded a Chi-square value of 91.011 (df = 10, p < .001), a -2 Log likelihood of 453.488, a Cox and Snell R-square value of .198, and a Nagelkerke R-square value of .270. These R-square values indicate that the model can explain between approximately 20% and 27% of the variance in the outcome. The model had a good fit with the data, as evidenced by the Hosmer and Lemeshow Goodness of Fit Test (Chi-square = 6.273, df = 8, p = .617). The final model accurately predicted 73.4% of cases across groups; however, the model predicted the retained students more accurately (89.6% of cases) than the non-retained cases (45.8% of cases).
Table 2 explains how each of the six variables retained in the model contributed to the final model. The odds ratio represents an association between a particular independent variable and a particular outcome, or for this study, the extent that the independent variables predict membership in the retained outcome group. With categorical variables, this odds ratio represents the likelihood that being in a category increases the odds of being in the retained group over the reference category (i.e., African American/Black participants were 1.779 times more likely to be in the retained group than White/Caucasian students, who served as the reference category). With continuous variables, odds ratios represent the likelihood that quantifiable changes in the independent variables predict membership in the retained group (i.e., for every unit increase in SAT math score, the odds of being in the retained group increase 1.004 times). The interpretation of odds ratios allows them to be viewed as a measure of effect size, with odds ratios closer to 1.0 having a smaller effect (Tabachnick & Fidell, 2013).
Table 2
Variables in the Equation for Hypothesis 1
|
|
|
|
|
95% C.I. for O.R. |
| Variable |
B |
S.E. |
Wald |
O.R. |
Lower |
Upper |
| Ethnicity |
|
|
10.319* |
|
|
|
| Ethnicity (African American/Black) |
.576 |
.393 |
2.148 |
1.779 |
.823 |
3.842 |
| Ethnicity (Hispanic) |
.068 |
.290 |
.054 |
1.070 |
.606 |
1.889 |
| Ethnicity (Asian/Pacific Islander) |
1.889 |
.637 |
8.803** |
6.615 |
1.899 |
23.041 |
| Ethnicity (Other) |
.258 |
.714 |
.131 |
1.295 |
.320 |
5.246 |
| Initial Major |
|
|
35.824*** |
|
|
|
| Initial Major (Declared STEM) |
.412 |
.265 |
2.422 |
1.511 |
.899 |
2.539 |
| Initial Major (Declared Non-STEM) |
-1.944 |
.375 |
26.905*** |
.143 |
.069 |
.298 |
| STEM Seminar (Non-CP) |
.850 |
.258 |
10.885** |
2.340 |
1.412 |
3.879 |
| SAT Math |
.004 |
.002 |
2.411 |
1.004 |
.999 |
1.008 |
| Math Placement–Algebra |
.002 |
.002 |
2.080 |
1.002 |
.999 |
1.005 |
| CTI Total Change |
.017 |
.007 |
5.546* |
1.017 |
1.003 |
1.032 |
| Constant |
-2.994 |
1.378 |
4.717 |
.050 |
|
|
Note: B = Coefficient for the Constant; S.E. = Standard Error; O.R. = Odds Ratio; * p < .05; ** p < .01; *** p < .001.
With logistic regression, the Wald Chi-square test allows the researcher to determine a coefficient’s significance to the model (Tabachnick & Fidell, 2013). Based on this test, Initial Major was the most significant predictor to the model (p < .001). Students in the initially Declared STEM category were 1.511 times more likely to be in the retained group than those in the initially Undeclared category (the reference category); the odds of being in the retained group decreased by a factor of .143 for students in the initially Declared Non-STEM group. The STEM course was the predictor with the second most statistical significance (p < .01), with students in the STEM seminar class being 2.340 times more likely to be in the retained outcome than those in the career planning class. The CTI Total Change score was statistically significant (p < .05), indicating that for every unit increase in CTI Total Change score (i.e., the larger the decrease in score from pretest to posttest), the odds of being in the retained group increase by a factor of 1.017. Ethnicity was a statistically significant predictor (p < .05), with each subcategory having higher odds of being in the retained group than the White/Caucasian group; however, the researchers caution the reader to read these odds ratios for ethnicity with caution because of the number of cases in some categories. SAT Math and Math Placement–Algebra were not statistically significant, but still fell within the recommended inclusion range (p < .20).
Hypothesis 2
Hypothesis 2 stated that the independent variables could predict undergraduate STEM retention from Year 1 to Year 3. As stated previously, the backward stepwise Wald approach involved including all predictors initially and then removing predictors one by one based on p value until all remaining predictors fell within the p ≤ .20 range. This process took six steps, resulting in the removal of five variables with p values greater than .20: (a) CTI Commitment Anxiety Change, (b) CTI Decision Making Confusion Change, (c) Gender, (d) CTI External Conflict Change, and (e) CTI Total Change, respectively. The model yielded a Chi-square value of 55.835 (df = 9, p < .001), a -2 Log likelihood of 307.904, a Cox and Snell R-square value of .191, and a Nagelkerke R-square value of .255. These R-square values indicate that the model can explain between approximately 19% and 26% of the variance in the outcome. The model had a good fit with the data, as evidenced by the Hosmer and Lemeshow Goodness of Fit Test (Chi-square = 9.187, df = 8, p = .327). The model accurately predicted 70.0% of cases across groups. In this analysis, the model predicted the non-retained students more accurately (72.7% of cases) than the retained cases (66.9% of cases).
Table 3 explains how the variables within the model contributed to the final model. Based on the Wald test, Initial Major was the most significant predictor to the model (p < .001). Students in the initially Declared STEM category were 1.25 times more likely to be in the retained group than those in the initially Undeclared category (the reference category); the odds of being in the retained group decreased by a factor of .167 for students in the initially Declared Non-STEM group. The Math Placement–Algebra variable was statistically significant (p < .05), and the odds ratios indicated that for every unit increase in Math Placement–Algebra test score, the odds of being in the retained group are 1.005 higher. The STEM course variable was slightly outside the statistically significant range but fell within the inclusion range, with students in the STEM seminar class being 2.340 times more likely to be in the retained outcome than students in the career planning class. SAT Math was not statistically significant but still fell within the recommended inclusion range (p < .20). Ethnicity also was not a statistically significant predictor but fell within the inclusion range, with each subcategory having higher odds of being in the retained group than the White/Caucasian group; however, the researchers caution the reader to read these odds ratios for ethnicity with caution because of the number of cases in some categories.
Table 3
Variables in the Equation for Hypothesis 2
|
|
|
|
|
95% C.I. for O.R. |
| Variable |
B |
S.E. |
Wald |
O.R. |
Lower |
Upper |
| Ethnicity |
|
|
6.445 |
|
|
|
| Ethnicity (African American/Black) |
.542 |
.448 |
1.467 |
1.719 |
.715 |
4.134 |
| Ethnicity (Hispanic) |
.243 |
.349 |
.484 |
1.275 |
.643 |
2.528 |
| Ethnicity (Asian/Pacific Islander) |
1.636 |
.698 |
5.494* |
5.137 |
1.307 |
20.185 |
| Ethnicity (Other) |
.403 |
.684 |
.347 |
1.497 |
.391 |
5.725 |
| Initial Major |
|
|
17.362** |
|
|
|
| Initial Major (Declared STEM) |
.223 |
.328 |
.460 |
1.250 |
.656 |
2.379 |
| Initial Major (Declared non-STEM) |
-1.792 |
.468 |
14.664** |
.167 |
.067 |
.417 |
| STEM Seminar (Non-CP) |
.588 |
.323 |
3.327 |
1.801 |
.957 |
3.389 |
| SAT Math |
.004 |
.003 |
2.536 |
1.004 |
.999 |
1.010 |
| Math Placement–Algebra |
.005 |
.002 |
5.449* |
1.005 |
1.001 |
1.009 |
| Constant |
-2.994 |
1.378 |
4.717 |
.050 |
|
|
Note: B = Coefficient for the Constant; S.E. = Standard Error; O.R. = Odds Ratio; * p < .05; *** p < .001.
Discussion
The researchers sought to determine the degree to which a set of demographic variables, math scores, and career-related factors could predict undergraduate retention in STEM majors. Based on descriptive statistics, the participants are remaining in STEM majors at a higher rate than other nationwide samples (Chen & Soldner, 2013; Koenig, Schen, Edwards, & Bao, 2012). The sample
in this study was quite different based on gender than what is commonly cited in the literature; approximately 46% of the study’s sample was female, whereas the NCSES (2017) reported that white females made up approximately 31% of those in STEM fields, with minority females lagging significantly behind. The present study’s sample was more in line with national statistics with regard to ethnicity (NCSES, 2017; Palmer et al., 2011).
With Hypothesis 1, the researchers sought to improve on a pilot study (Belser et al., 2017) that did not include demographics or math-related variables. Adding these additional variables did improve the overall model fit and the accuracy of predicting non-retained students, but slightly decreased the accuracy of predicting retained students, as compared to the Belser et al. (2017) model. In addition to improving the model fit, adding in additional variables reversed the claim by Belser et al. (2017) that students in the STEM-focused career planning class were more likely to be retained than the STEM seminar students. In the present study, the STEM seminar students, who declared STEM majors prior to the first day of college, were more likely to be retained in STEM majors, which is in line with prior research connecting intended persistence in a STEM major to observed retention (Le et al., 2014; Lent et al., 2016).
With Hypothesis 2, the researchers sought to expand on the Belser et al. (2017) study by also predicting retention one year farther, into the third year of college. In this endeavor, the analysis yielded a model that still fit the data well. However, this model was much more accurate in predicting the non-retained students and was slightly less accurate in predicting the retained students, with the overall percentage of correct predictions similar to Hypothesis 1. This finding indicates that the included predictors may provide a more balanced ability to predict long-term retention in STEM majors than in just the first year. The initial major and STEM course variables performed similarly as in Hypothesis 1, and as such, similarly to prior research (Le et al., 2014; Lent et al., 2016).
Although sampling issues warrant the reader to read ethnicity results with caution, ethnicity did show to be a good predictor of retention in STEM majors with both Hypotheses 1 and 2. More noteworthy, the African American/Black and Hispanic students had higher odds of being retained. This is inconsistent with most research that shows underrepresented minorities as less likely to be retained in STEM majors (Chen & Soldner, 2013; Cundiff et al., 2013; Gayles & Ampaw, 2014); however, at least one study has previously found results in which ethnic minority students were more likely to be retained in STEM majors (Riegle-Crumb & King, 2010).
Gender was removed as a predictor from both models because of its statistical non-significance. Prior research has shown that females are less likely to be retained in STEM majors (Cundiff et al., 2013; Gayles & Ampaw, 2014; Riegle-Crumb et al., 2012), which separates this sample from prior studies. However, the COMPASS sample did have a larger representation of females than typically observed. Moreover, the COMPASS Program has been mindful of prior research related to gender and took steps to address gender concerns in program development (Dagley et al., 2016).
The continuous variables retained in the models showed only a mild effect on predicting STEM retention. The SAT Math and Math Placement–Algebra scores did perform consistently with prior research, in which higher math scores related to higher odds of retention (CollegeBoard, 2012; Crisp et al., 2009; Le et al., 2014; Mattern & Patterson, 2013; Rohr, 2012). The CTI variables that were retained in the models performed in line with the Belser et al. (2017) pilot study specific to STEM majors and with prior research examining negative career thoughts in undergraduate retention in other majors (Folsom, Peterson, Reardon, & Mann, 2005; Reardon et al., 2015).
Limitations and Implications
The present study has limitations, particularly with regard to research design, sampling, and instrumentation. First, the researchers used a comparison group design rather than a control group, and as such, there were certain observable differences between the two groups. Not having a control group limits the researchers’ ability to make causal claims regarding the predictor variables or the STEM career intervention. The researchers also only included a limited number of predictors; the inclusion of additional variables may have strengthened the models. Although the sample size was sufficient based on the a priori power analysis, the low number of participants in some of the categories may have resulted in overfitting or underfitting within the models. Finally, the researchers were not able to test psychometric properties of the SAT Math subtest or the Math Placement–Algebra subtest with this sample because of not having access to the participants’ item responses for each. The researchers attempted to mitigate limitations as much as possible and acknowledge that they can and should be improved upon in future research.
Future research in this area would benefit from the inclusion of a wider variety of predictor variables, such as math and science self-efficacy, outcome expectations, and internal processes observed with gender and ethnic minority groups (e.g., stereotype threat; Cundiff et al., 2013; Litzler et al., 2014). The researchers also recommend obtaining a larger representation of ethnic minority groups to ensure an adequate number of cases to effectively run the statistical procedure. Future researchers should consider more complex statistical procedures (e.g., structural equation modeling) and research designs (e.g., randomized control trials) to determine more causal relationships between predictors and the outcome variables.
Because the results of this study indicate that a more solidified major selection is associated with higher odds of retention in STEM majors, university career professionals and higher education professionals should strive to develop programming that helps students decide on a major earlier in their undergraduate careers. Structured career development work, often overlooked in undergraduate STEM programming, may be one such appropriate strategy. Additionally, any undergraduate STEM programming must be sensitive to demographic underrepresentation in STEM majors and the STEM workforce and should take steps to provide support for students in these underrepresented groups.
Similar to work with undergraduates, this study’s results provide a rationale for school counselors to engage students in STEM career work so that they can move toward a solidified STEM major prior to enrolling in college. The industry-specific career development work discussed within this study is just as important, if not more important, for students in K–12 settings. Moreover, school counselors, through their continued access to students, can serve as an access point for researchers to learn more about the STEM career development process at an earlier stage of the STEM pipeline. All of these endeavors point to the need for counselor educators to better prepare school counselors, college counselors, and career counselors to do work specifically with STEM and to become more involved in STEM career research.
In the present study, the researchers built upon prior research in the area of STEM retention to determine which variables can act as predictors of undergraduate STEM retention. The binary logistic regression procedure yielded two models that provide insight on how these variables operate individually and within the larger model. Finally, the researchers identified some key implications for counselors practicing in various settings and for researchers who are interested in answering some of the key questions that still exist with regard to STEM career development and retention.
Conflict of Interest and Funding Disclosure
Data collected in this study was part of a dissertation study by the first author. The dissertation was awarded the 2018 Dissertation Excellence Award by the National Board for Certified Counselors.
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Christopher T. Belser, NCC, is an assistant professor at the University of New Orleans. M. Ann Shillingford is an associate professor at the University of Central Florida. Andrew P. Daire is a dean at Virginia Commonwealth University. Diandra J. Prescod is an assistant professor at Pennsylvania State University. Melissa A. Dagley is an executive director at the University of Central Florida. Correspondence can be addressed to Christopher Belser, 2000 Lakeshore Drive, Bicentennial Education Center Room 174, New Orleans, LA 70148, ctbelser@uno.edu.
Sep 1, 2018 | Volume 8 - Issue 3
Nayoung Kim, Glenn W. Lambie
To prevent school counselors from experiencing feelings of burnout, identifying relevant factors is important. The purpose of this article is to review studies investigating the constructs of burnout and occupational stress in school counseling samples. Eighteen published research articles fit the inclusion criteria for this review. The researchers identified external and internal variables relating to school counselor burnout, as well as protective and risk factors. The review identified that school counselors’ higher level of burnout correlated with having non-counseling duties, being assigned large caseloads, working in schools that did not meet adequate yearly progress (AYP) status, experiencing a lack of supervision, possessing greater emotion-oriented stress coping scores, providing fewer direct student services, and having greater perceived stress. In contrast, feelings of burnout among school counselors were mitigated when counselors received supervision, possessed higher task-oriented stress coping strategies, scored at higher levels of ego maturity, reported greater occupational support at their schools, had greater grit scores, and worked in schools that met AYP.
Keywords: burnout, occupational stress, school counselors, non-counseling duties, coping strategies
There are multiple definitions of burnout (e.g., Burke & Richardson, 2000; Stalker & Harvey, 2002); however, the primary consistent aspect of burnout is that it is a psychological phenomenon associated with job-related stress (Maslach, 2017). Burnout occurs when professionals are unable to meet their own needs, as well as their clients’ needs, in a high-pressure environment (Maslach, 2017). Freudenberger (1990) identified common symptoms of burnout, including negative changes in individuals’ (a) attitudes and decision making; (b) physiological states; (c) mental, emotional, and behavioral health; and (d) occupational motivation. Burnout has significant consequences, including compromised physical health, increased risk of mental health disorders (e.g., depression, substance abuse), poor job performance, absenteeism, occupational attrition, and low self-esteem (Maslach & Leiter, 2016). Burnout can also cause symptoms such as fatigue, exhaustion, and insomnia (Armon, Shirom, Shapira, & Melamed, 2008).
Burnout in School Counseling
Morse, Salyers, Rollins, Monroe-DeVita, and Pfahler (2012) identified that 21% to 67% of mental health professionals reported experiencing high levels of burnout, possibly because of dealing with high client caseloads (Ducharme, Knudsen, & Roman, 2007) or overall job effectiveness (Stalker & Harvey, 2002). In addition, Oddie and Ousley (2007) found that 21% to 48% of mental health workers reported experiencing high levels of emotional exhaustion. School counselors specifically are at risk for experiencing feelings of burnout because of their multiple job demands, including paperwork, parent conferences, school-wide testing, large caseloads, and requests from administrators (McCarthy & Lambert, 2008), and other factors such as role ambiguity and limited occupational support (Young & Lambie, 2007). The school counseling job environment, where “the demands of the work are high, but the resources to meet those demands are low” (Maslach & Goldberg, 1998, pp. 63–64), increases susceptibility to experiencing feelings of burnout (e.g., average student-to-counselor ratio being 491-to-1; National Center for Education Statistics, 2016). Stephan (2005) found that within a national sample of school counselors, 66% of middle school counselors scored at moderate to high levels of emotional exhaustion. Further, Wachter (2006) found that 20% of the school counselors in her investigation (N = 132) experienced feelings of burnout; 16% scored at moderate levels of burnout, and 4% scored at severe levels of burnout. Thus, many school counselors experience feelings of burnout that may influence their ability to provide ethical and effective counseling services to the students they serve.
School counselors may experience chronic fatigue, depersonalization, or feelings of hopelessness and leave their jobs because of the rigidity of school systems and limited support (Young & Lambie, 2007). In fact, counselors experiencing significant feelings of burnout provide reduced quality of service to their clientele because burnout relates to lower productivity, turnover intention, and a lowered level of job commitment (Maslach, Schaufeli, & Leiter, 2001). Because of the importance of preventing the burnout phenomenon, the American School Counselor Association’s (ASCA; 2016) ethical standards note that school counselors are responsible for maintaining their health, both physically and emotionally, and caring for their wellness to ensure their effective practice. The American Counseling Association’s (2014) ethical standards also state that school counselors have an ethical responsibility to monitor their feelings of burnout and remediate when their feelings potentially influence their ability to provide quality services to their stakeholders. To monitor burnout, counselors need to understand the symptoms of burnout and prevent it from happening, while maintaining their psychological well-being.
School counselors face challenges with their significant job demands (McCarthy, Van Horn Kerne, Calfa, Lambert, & Guzmán, 2010), such as large caseloads (Lambie, 2007) and extreme amounts of non-counseling duties (Moyer, 2011). In fact, school counselors report job stress and dissatisfaction when they are required to complete non-counseling duties, hindering their ability to work with their students (McCarthy et al., 2010). Examples of non-counseling duties include clerical tasks, such as scheduling students for classes; fair share, such as coordinating the standardized testing program; and administrative duties, such as substitute teaching (Scarborough, 2005). School counselors with large caseloads and high student-to-counselor ratios are more likely to experience increased feelings of burnout (Bardhoshi, Schweinle, & Duncan, 2014). Although ASCA (2015) recommends a student-to-counselor ratio of 250-to-1, the U.S. average student-to-counselor ratio is almost double the recommended proportion (491-to-1; National Center for Education Statistics, 2016).
Insufficient resources for school counselors and negative job perception increase their likelihood of experiencing feelings of burnout. Lower levels of principal support and lack of clinical supervision raise school counselors’ occupational stress (Bardhoshi et al., 2014; Moyer, 2011). For instance, school counselors with higher levels of role ambiguity are likely to experience burnout (Wilkerson & Bellini, 2006). School counselors experience role ambiguity when their responsibilities or the expected level of performance is not clearly identified (Coll & Freeman, 1997). As a result, school counselors report increased levels of stress (Culbreth, Scarborough, Banks-Johnson, & Solomon, 2005), leading to burnout and attrition from the profession (Wilkerson & Bellini, 2006). ASCA (2016) dictated that school counselors’ responsibilities include providing counseling services to students to support their development, which distinguishes them from other school personnel. With the importance of preventing burnout in school counseling, the purpose of this review is twofold: (a) to present identified factors influencing school counselors’ levels of burnout and (b) to offer strategies to assist school counselors in mitigating the feelings of burnout.
Research Examining Burnout in School Counseling
We began by conducting a formal search of electronic databases—PsycINFO, ERIC (EBSCOhost), and Academic Search Premiere—relating to school counselor burnout. The search term burnout was first used to analyze the research trend in the field. Both the search terms burnout and school counselors OR school counseling were used to collect any articles on the topic of school counselor burnout published between 2000 and 2018. An additional search was conducted with the terms occupational stress and school counselors OR school counseling to identify potential studies related to the topic in the same type of literature.
The following inclusion criteria were applied for our review: (a) investigations of school counselor burnout and occupational stress, (b) sample participants were school counselors in the United States, (c) the primary topic of the investigation was burnout and/or occupational stress, (d) articles were written in English, (e) articles were published in refereed journals, and (f) articles were published between 2000 and 2018. In addition, our review excluded literature reviews, editorials, and rejoinders. The abstracts of the articles meeting the criteria were examined and confirmed in order to be included in our review.
Our literature search based on the inclusion criteria produced 51 articles. As not all articles from the search satisfied the criteria, the articles were reviewed manually to evaluate whether they met the criteria, resulting in 35 articles not meeting criteria (e.g., conceptual articles, studies related to teachers) and 16 articles meeting all criteria. An additional literature search yielded two more studies meeting the inclusion criteria, identifying 18 studies in total. None of the identified research articles examined prevention or treatment interventions for burnout in school counselors. The 18 investigations had school counselor burnout or occupational stress as the constructs of interest. The research findings identified the positive relationships between school counselors’ burnout or occupational stress scores and the following factors: (a) non-counseling duties, (b) large caseloads, (c) not meeting adequate yearly progress (AYP) status (i.e., the expected amount of students’ academic growth per year based on the No Child Left Behind mandate [Minnesota House of Representatives, 2003]), (d) lack of supervision, (e) emotion-oriented stress coping scores, (f) grit, and (g) perceived stress.
Fourteen out of 18 articles provided information related to school counselor burnout (see Table 1 for quantitative studies and Table 2 for qualitative studies), and the other four studies investigated school counselors’ occupational stress (see Table 3). Occupational stress refers to the strain a person experiences when the perceived stress in a workplace outweighs their ability to cope (Decker & Borgen, 1993). Quantitative research methods were employed in 15 of the investigations, two used mixed-methods, and one study utilized a qualitative approach. For all 18 articles, the participants were current school counselors, and the number of participants ranged from 3 to 926. Effect sizes were categorized depending on the analysis into three groups (i.e., small, medium, and large) based on the effect size matrix from Sink and Stroh (2006), offering a better understanding of the results. Specifically, the effect size from independent samples t-test (2 groups; Cohen’s d) is interpreted as small for 0.2, medium for 0.5, and large for 0.8. For the effect size of other analyses listed in this review, including paired-samples t-tests (η2), multiple regression (R2), and analysis of variance (ANOVA; η2), 0.01 is considered as small, 0.06 as medium, and 0.14 as large.
Table 1
Summary of Quantitative/Mixed Studies Related to Professional School Counselor (PSC) Burnout
| Study |
Sample |
Variables |
Findings |
| Bain, Rueda, Mata-Villarreal, & Mundy (2011) |
PSCs in rural districts of South Texas
(N = 27)
Convenient Sampling |
Mental health awareness, the amount of time spent on academic advising
|
Feelings of burnout were reported by the majority of the PSCs (89%) in the study and many of them spent the greatest amount of time on administrative duties and the least on counseling. |
| Bardhoshi, Schweinle, & Duncan (2014) |
PSCs
(N = 212)
Random Sampling |
Non-counselor duties, school factors, five subscales of the CBI |
Non-counseling duties and school factors were associated with PSC burnout. Non-counseling duties explained the variance of the three burnout subscales: Exhaustion (11%; medium effect size), NWE (6%; medium effect size), and DPL (8%; medium effect size). Non-counseling duties and other factors (e.g., caseload, principal support) explained the variance of the four burnout subscales: Exhaustion (21%; large effect size), Incompetence (9%; medium effect size), NWE (49%; large effect size), and DPL (17%; large effect size). |
| Butler & Constantine (2005) |
PSCs
(N = 533)
Random Sampling |
Collective self-esteem, burnout, demographics |
Collective self-esteem explained 3% of the variance of PSC burnout (small effect size). In particular, PRCS (2%) and PUCS (1%) accounted for PA (both small effect sizes), and IICS explained 1% of feelings of DP and PA (both small effect sizes). Higher collective self-esteem was associated with lower PSC burnout. PSCs working in urban settings tended to have higher levels of burnout than the counterparts in other environmental settings. PSCs with experience of 20–29 years reported higher levels of burnout than the counterparts with 0–9 years of experience. PSCs with experience of 30 or more years reported higher levels of burnout than those with less experience. |
| Gnilka, Karpinski, & Smith (2015) |
PSCs
(N = 269)
Convenient Sampling |
Five subscales on the CBI |
Effect size differences were found between PSCs and other professionals in the counseling fields (Exhaustion, d = .26, small effect size; DC, d = -.50, medium effect size). Effect size differences were noted between PSCs and sexual offender and sexual abuse therapists (Exhaustion, d = .27, small effect size; DPL, d = -.23, small effect size; DC, d = -.82, large effect size). |
| Lambie (2007) |
PSCs
(N = 218)
Random Sampling
|
Ego maturity, three subscales on the MBI-HSS
|
PSCs with greater levels of ego maturity tended to have a higher level of PA than those with lower ego maturity. Ego maturity predicted PA (3.3%; small effect size). Occupational support and the subscales of burnout were correlated. Reported occupational support predicted EE (16%; large effect size), DP (12%; medium effect size), and PA (7.2%; medium effect size). |
| Limberg, Lambie, & Robinson (2016-2017) |
PSCs
(N = 437)
Random Sampling/
Purposive Sampling |
Altruistic motivation, altruistic behavior, burnout |
PSCs with greater levels of altruism had lower levels of EE and higher feelings of PA. PSC altruism explained 31.36% of the variance in EE (large effect size), and 29.16% of the variance in PA (large effect size). Self-Efficacy accounted for 14.4% of the variance in EE (large effect size) and 9% of the variance in PA (medium effect size). |
| Moyer (2011) |
PSCs
(N = 382)
Convenient Sampling |
Non-guidance activities, supervision, student-to-counselor ratios, five subscales of the CBI |
Non-guidance–related duties and clinical supervision were significant predictors of PSC burnout. Non-guidance duties (7.3%; medium effect size) and supervision (9%; medium effect size) predicted burnout.
|
| Mullen, Blount, Lambie, & Chae (2017) |
PSCs
(N = 750)
Random Sampling |
Perceived stress, burnout, job satisfaction |
Perceived stress predicted burnout positively (large effect size) and job satisfaction negatively (large effect size). Perceived stress and burnout predicted job satisfaction (large effect size). Burnout mediated the relationship between perceived stress and job satisfaction. |
| Mullen & Crowe (2018) |
PSCs
(N = 330)
Convenient Sampling |
Grit, stress, burnout |
Grit was negatively related to burnout (small effect size) and stress (small to medium effect size). |
| Mullen & Gutierrez (2016)
|
PSCs
(N = 926)
Random Sampling
|
Burnout, perceived stress, direct student services
|
Burnout attributed to direct counseling activities (12%; medium effect size), direct curriculum activities (5%; small to medium effect size), and percentage of time at work providing direct services to students (6%; medium effect size). |
| Wachter, Clemens, & Lewis (2008) |
PSCs
(N = 249)
Random Sampling |
Demographics, stakeholder involvement, lifestyle themes, burnout |
Burnout and lifestyle themes were associated. Perfectionism subscale was negatively related to burnout, and the Self-Esteem subscale was positively related to PSC burnout. About 15.1% of the variance in burnout was accounted for by the lifestyle themes of Self-Esteem and Perfectionism (large effect size). |
| Wilkerson & Bellini (2006)
|
PSCs in northeastern U.S.
(N = 78)
Systematic Random Sampling
|
Demographics, intrapersonal, and organizational factors; three subscales on the MBI-ES |
Demographic (age, counseling experience, supervision, and student/counselor ratio), intrapersonal, and organizational factors significantly accounted for the amount of the variance in each subscale of burnout, including EE (45%; large effect size), DP (30%; large effect size), and PA (42%; large effect size). |
| Wilkerson (2009)
|
PSCs
(N = 198)
Random Sampling |
Demographic and organizational stressors and individual coping strategies; three subscales on the MBI-ES |
Demographic factors (years of experience and student/counselor ratio), organizational stress, and coping styles explained the variance of each subscale of burnout including EE (49%; large effect size), DP (27%; large effect size), and PA (36%; large effect size).
|
Table 2
Summary of Qualitative/Mixed Studies Related to Professional School Counselor Burnout
| Study |
Sample |
Topic |
Identified Themes |
| Bain, Rueda, Mata-Villarreal, & Mundy (2011) |
PSCs in rural districts of South Texas (N = 27)
Convenient Sampling |
Helpful ways to better provide mental health services at school |
Having access to additional staff and additional education and awareness in terms of helpful ways to provide mental health services at their school. |
| Bardhoshi, Schweinle, & Duncan (2014) |
PSCs
(N = 252)
Random Sampling |
a) Their experience of burnout
b) The meaning of performing non-counseling duties |
a) Lack of time, budgetary constraints, lack of resources, lack of organizational support, etc.
b) Adverse personal/professional effects, a reality of the job, reframing the duties within the context of the job. |
| Sheffield & Baker (2005) |
Female PSCs
(N = 3)
Purposive Sampling |
Burnout experience |
Important beliefs, burnout feelings, burnout attitude, (lack of) collegial support. |
Table 3
Summary of Quantitative Studies Related to Professional School Counselor Occupational Stress
| Study |
Sample |
Variables |
Findings |
| Bryant & Constantine (2006) |
Female PSCs
(N = 133)
Random Sampling |
Role balance, job satisfaction, satisfaction with life, demographics |
Multiple role balance ability and job satisfaction positively predicted overall life satisfaction. Role balance and job satisfaction explained the variance of life satisfaction (41%; large effect size). |
| Culbreth, Scarborough, Banks-Johnson, & Solomon (2005) |
PSCs
(N = 512)Stratified Random Sampling |
Role conflict, role ambiguity, role incongruence, demographics |
Perceived match between the job expectations and actual experiences predicted role-related job stress, including role conflict (7.6%; medium effect size); role incongruence (19.7%; large effect size); and role ambiguity (8.3%; medium effect size). |
| McCarthy, Van Horn Kerne, Calfa, Lambert, & Guzmán (2010) |
PSCs in Texas
(N = 227) Convenient Sampling |
Demographics, job stress, resources and demands |
Job stress was different between the resourced, balanced, and demand groups. The effect sizes were large in the differences between the demand group and the resourced group (1.62; large effect size) and the balanced group (0.70; large effect size).
|
| Rayle (2006) |
PSCs
(N = 388)Convenient Sampling |
Demographics, mattering, job-related stress |
Thirty-five percent of the variance in overall job satisfaction was explained by mattering to others at work and job-related stress (large effect size). Mattering to others (19.36%; large effect size) and job-related stress (16.81%; large effect size) explained the variance in overall job satisfaction. |
Three instruments were used to measure levels of school counselor burnout, including: (a) the Maslach Burnout Inventory (MBI; Maslach, Jackson, & Leiter, 1996), (b) the Counselor Burnout Inventory (CBI; S. M. Lee et al., 2007), and (c) the Burnout Measure Short Version (BMS; Malach-Pines, 2005). Maslach and Jackson (1981) defined burnout with three dimensions: Emotional Exhaustion (EE), Depersonalization (DP), and reduced Personal Accomplishment (PA). Emotional exhaustion is to exhaust one’s capacity to continuously involve with clients (R. T. Lee & Ashforth, 1996). Not being able to respond to clients’ needs may cause counselors to distance themselves from their job emotionally and cognitively, which is defined as depersonalization. Lastly, having a lower sense of effectiveness may reduce feelings of personal accomplishment (Maslach et al., 2001). Four studies used the MBI-Education Survey (MBI-ES), which was designed for the education population, and another study utilized the MBI-Human Services Survey (MBI-HSS), in which the word students from the MBI-ES is substituted with recipients in a third of the items (Sandoval, 1989).
Four studies used the CBI, which is a 20-item instrument with five subscales, including:
(a) Exhaustion, (b) Incompetence, (c) Negative Work Environment (NWE), (d) Devaluing Client (DC), and (e) Deterioration in Personal Life (DPL). Exhaustion is the condition of being physically and emotionally exhausted by the duties of a counselor, and incompetence focuses on counselors’ feelings of being incompetent. While negative work environment refers to the stress caused by the working environment, devaluing client is related to being unable to establish emotional connectedness with clients. Finally, deterioration in personal life assesses the level of deterioration in a counselor’s personal life. Sample items include “I feel exhausted due to my work as a counselor,” and “I feel I have poor boundaries between work and my personal life.” The internal consistency of the CBI ranged from .73 to .85 (S. M. Lee et al., 2007). In addition, three studies used the BMS (Malach-Pines, 2005), a 10-item scale in which participants rate their answers to the question “When you think about your work overall, how often do you feel the following?” in seven prompts, including: “Trapped,” “Hopeless,” and “Helpless.” The BMS is adapted from the original version of the Burnout Measure (Pines & Aronson, 1988). The internal consistency of the BMS ranged from .85 to .87 (Malach-Pines, 2005).
Researchers investigated different factors relating to school counselor burnout within the 18 published articles. One of the studies provided descriptive statistics of school counselor burnout, comparing school counselors to other mental health professionals and showing how burnout symptoms may emerge (N = 269; Gnilka, Karpinski, & Smith, 2015). School counselors had greater levels of Exhaustion (d = .26; small effect size) and lower levels of DC (d = -.50; medium effect size) than mental health professional participants. Furthermore, school counselors had greater levels of Exhaustion (d = .27; small effect size) and lower levels of DC (d = -.82; large effect size) compared to the mental health professional participants working with sex offenders and clients that have been sexually abused. Therefore, school counselors score higher in exhaustion as compared to other mental health professionals and score lower on devaluing their clients.
Individual Factors Related to Burnout
The two categories of individual factors relating to school counselor burnout were (a) psychological constructs and (b) demographic factors. The psychological constructs included ego maturity (Lambie, 2007), collective self-esteem (Butler & Constantine, 2005), altruism (Limberg, Lambie, & Robinson, 20162017), lifestyle themes (Wachter, Clemens, & Lewis, 2008), coping styles (Wilkerson, 2009), perceived stress (Mullen, Blount, Lambie, & Chae, 2017), and grit (Mullen & Crowe, 2018). The definitions of these psychological constructs related to school counselor burnout follow.
Ego maturity refers to the fundamental element of an individual’s personality, encompassing components of self, social, cognitive, character, and moral development (Loevinger, 1976). When individuals’ egos develop, they become more individualistic, autonomous, and highly aware of themselves (Loevinger, 1976). Collective self-esteem is individuals’ perception of their identification with the social group they belong to (Bettencourt & Dorr, 1997). Altruism is the behavior driven by values or goals individuals possess or their concerns for others, aside from external rewards (Eisenberg et al., 1999). A lifestyle is an individual’s way of perceiving self, others, and the world (Mosak & Maniacci, 2000), and lifestyle themes refer to common patterns people possess in relation to their lifestyles (Mosak, 1971). Coping is defined as cognitive and behavioral efforts to deal with specific demands that take up or exceed individuals’ resources (Lazarus & Folkman, 1984), and coping styles refer to individuals’ relatively stable patterns in handling stress (Heszen-Niejodek, 1997). Perceived stress represents the extent to which individuals evaluate their situations as stressful (Cohen, 1986). Grit is “perseverance and passion for long-term goals” (Duckworth, Peterson, Matthews, & Kelly, 2007, p. 1087). Specifically, grit refers to efforts to achieve a goal despite challenges. In addition to psychological constructs, the demographic factors category included years of experience in school counseling (Butler & Constantine, 2005; Wilkerson, 2009; Wilkerson & Bellini, 2006) and age (Wilkerson & Bellini, 2006).
Psychological constructs. Seven studies identified that psychological constructs relate to school counselors’ feelings of burnout. Five of seven factors had large effect sizes, including ego maturity, altruism, lifestyle themes, coping styles, and grit, and three of the factors with large effect sizes were associated with Emotional Exhaustion (EE) among the MBI (Maslach et al., 1996) subscale scores (i.e., ego maturity, altruism, and coping styles).
Specifically, Lambie (2007) examined the directional relationship between school counselors’
(N = 218) burnout and ego maturity, identifying that those counselors with higher levels of ego maturity were likely to have greater feelings of Personal Accomplishment (PA; R2 = .033). The researcher also investigated the relationship between the school counselors’ reported occupational support and their MBI burnout subscales scores (Maslach & Jackson, 1996), identifying that each MBI subscale relates to the participants’ levels of reported occupational support; EE (large effect size; R2 = .167); DP (medium effect size; R2 = .120); and PA (medium effect size; R2 = .072). The results indicated that school counselors scoring at higher ego maturity levels had lower feelings of burnout, and counselors experiencing high levels of occupational support had significantly lower burnout scores.
The relationship between burnout and collective self-esteem was investigated within a sample of school counselors (N = 533; Butler & Constantine, 2005). The Collective Self-Esteem Scale has four subscales (Luhtanen & Crocker, 1992), including (a) Private Collective Self-Esteem (PRCS), (b) Public Collective Self-Esteem (PUCS), (c) Membership Collective Self-Esteem (MCS), and (d) Importance to Identity Collective Self-Esteem (IICS). These subscales measure individuals’ perception of social groups they belong to, including how they feel about the group (PRCS), how they perceive others feel about the group (PUCS), how they perceive themselves being a good member of the group (MCS), and how important their social group is to their self-concept (IICS). These four Collective Self-Esteem Scale subscales explained 3% of the variance in the burnout subscales (Pillai’s trace = .08, F [12, 1584] = 3.48, p < .001, η2M = .03; Maslach & Jackson, 1986).
In general, higher collective self-esteem relates to lower levels of burnout, and different dimensions of collective self-esteem relate to different components of burnout. Higher PRCS was associated with higher feelings of PA (η2 = .02), and higher PUCS was related to lower levels of EE (η2 = .01). The school counselors’ IICS subscale scores were related to their lower feelings of DP (η2 = .01) and greater feelings of PA (η2 = .01). Although a small amount of variance in burnout scores (.01–.02) was explained by the components of collective self-esteem, the positive relationship between higher PRCS and higher feelings of PA identified that positive perceptions of the group school counselors belong to might reduce their feelings of burnout. For instance, having a sense of pride as a school counselor by observing other school counselors’ hard work and good relationships with students may promote their sense of PRCS, which may lead to higher feelings of PA. Taken together, promoting school counselors’ collective self-esteem may decrease their feelings of burnout.
Limberg and colleagues (2016–2017) investigated the directional relationship between school counselors’ (N = 437) levels of altruism and burnout. The school counselors with greater levels of altruism had lower levels of EE and higher feelings of PA. Specifically, the altruism subscales of Positive Future Expectation (PFE) and Self-Efficacy from the Self-Report Altruism Scale (Rushton, Chrisjohn, & Fekken, 1981) and two subscales of burnout (MBI) correlated (χ2 = 403.611, df = 216, χ2 ratio = 1.869, p < .001). PFE and Self-Efficacy accounted for 31.36% of the variance in the EE subscale (large effect size), and 29.16% of the variance in the PA subscale (large effect size). The Self-Efficacy subscale, which involves individuals’ perceived competence in a certain skill, explained 14.4% of the variance in EE subscale scores (large effect size), and 9% of the variance in PA subscale scores (medium effect size). Therefore, the results identified that school counselors’ levels of altruism negatively contribute to their burnout scores.
Burnout was related to lifestyle themes among school counselors (N = 249; Wachter et al., 2008). Two subscales of lifestyle themes from the Kern Lifestyle Scale (Kern, 1996), Self-Esteem and Perfectionism, accounted for 15.1% of the variance in burnout (large effect size; R2 = .151). Specifically, the Perfectionism subscale was negatively related to school counselor burnout scores (Burnout Measure: Short Version; BMS; Malach-Pines, 2005), and the Self-Esteem subscale was positively related to school counselor burnout. As a result, these findings identified school counselors’ personality factors relating to their risk of burnout, supporting that higher levels of perfectionism and lower levels of self-esteem may increase the likelihood of experiencing burnout.
Two studies employed hierarchical regression analyses to examine what factors may predict burnout subscale scores of the MBI, and one of the predicting variables was coping styles (Wilkerson, 2009; Wilkerson & Bellini, 2006). Wilkerson (2009) used four-step hierarchical regression models that included demographics, organizational stressors, and coping strategies, such as task-oriented, emotion-oriented, and avoidance-oriented coping (N = 198). The models with large effect sizes explained all three MBI burnout subscales. Specifically, 49% of the variance in the EE subscale was explained (large effect size; R2 = .49); 27% of the variance in the DP subscale was accounted for (large effect size; R2 = .27); and 36% of the variance of the PA subscale was explained (large effect size; R2 = .36). The results identified school counselors’ stressor scores both at the individual and organizational levels; intrapersonal coping strategies contributed to feelings of burnout with large effect sizes in the final model. In other words, demographic factors (e.g., more school counseling experience), coping styles (e.g., more emotion-oriented and less task-oriented coping strategies), and organizational variables (e.g., lack of decision-making authority, role ambiguity, role incongruity, and role conflict) positively predicted the level of burnout among school counselors.
Wilkerson and Bellini (2006) used three-step hierarchical regression models including demographic, intrapersonal, and organizational factors to examine the relationship between the variables and burnout among school counselors (N = 78). The school counselors’ demographic data (e.g., age, counseling experience, supervision, and student/counselor ratio), and intrapersonal (i.e., coping strategies) and organizational factors (e.g., role conflict, role ambiguity, and counselor occupational stress) significantly accounted for the variance in their burnout subscale scores on the MBI. Specifically, 45% of the variance in the EE subscale was explained (large effect size; R2 = .45), 30% of the variance in the DP subscale was accounted for (large effect size; R2 = .30), and 42% of the variance in the PA subscale was explained (large effect size; R2 = .42) by the final three-step model with the variables (i.e., counselor demographics, intrapersonal factors, and organizational factors). The findings indicated that school counselors’ emotion-oriented coping style predicted their three MBI subscale scores, supporting the importance of utilizing helpful strategies (i.e., task-oriented coping) to mitigate counselors’ feelings of burnout.
Another study examined how school counselors’ perceived stress and job satisfaction relate to burnout (Mullen et al., 2017). Specifically, perceived stress measured by the Perceived Stress Scale (Cohen, Kamarck, & Mermelstein, 1983) explained 52% of the variance in burnout (F (1, 749) = 808.55, p < .001; R2 = .52) and 25% of the variance in job satisfaction (F (1, 749) = 243.36, p < .001; R2 = .25). When both perceived stress and burnout were examined in order to test the relationship with job satisfaction, they explained 40% of the variance in job satisfaction (F (2, 747) = 246.48, p < .001; R2 = .40). In addition, the results indicated that burnout mediated the relationship between perceived stress and job satisfaction (z = -21.47, p < .001), and burnout (rs = .99) predicted job satisfaction better than perceived stress (rs = .79). Overall, perceived stress predicted burnout positively (large effect size) and job satisfaction negatively (large effect size). Both perceived stress and burnout predicted job satisfaction (large effect size).
Finally, Mullen and Crowe (2018) investigated the relationship between grit, burnout, and stress among school counselors (N = 330). The researchers found that grit was negatively correlated with burnout (r = -.22, p < .001) and stress (r = -.28, p < .001). Specifically, perseverance of effort, one of the subscales from the Grit-S (Duckworth & Quinn, 2009), was negatively related with burnout (r = -.12,
p < .05) and stress (r = -.19, p < .001). Therefore, school counselors’ level of grit may be a protective factor for burnout and stress.
Demographic factors. School counselors’ individual factors, such as age (Wilkerson & Bellini, 2006) and years of experience (Butler & Constantine, 2005; Wilkerson, 2009), correlate with feelings of burnout. Age was negatively correlated to the DP subscale (r = -.19, p < .05); therefore, older school counselors were less likely to experience burnout as compared to younger counselors (Wilkerson & Bellini, 2006). Nevertheless, the correlation between school counselors’ years of experience and burnout was inconsistent. Wilkerson and Bellini (2006) indicated that years of experience negatively correlated with the EE (r = -.26, p < .01), and DP (r = -.24, p < .05) subscales, while Butler and Constantine (2005) identified that school counselors with more years of experience scored at higher levels of burnout (MBI scores). Specifically, school counselors with 20–29 years of experience had greater DP subscale scores than those with 0–9 years of experience (F (3, 529) = 3.38, p < .05); and counselors with 30 years or more of experience had lower PA subscale scores than those with less than 20 years of experience (F (3, 529) = 3.39, p < .05). Furthermore, Wilkerson (2009) also reported that the years of experience positively correlated with the EE (ß = .21, p < .01) and DP (ß = .26, p < .01) MBI subscales in the hierarchical regression models whose variables included counselor demographics and organizational and intrapersonal variables to explain the variance of the burnout scores. Possible reasons behind the incongruent results may relate to school counselors’ role ambiguity, as counselors with less experience may experience or perceive large workloads compared to more experienced counselors. The conflicting results also may be related to other school counselor factors, such as the level of social support counselors experience at their schools. The findings identified the need for more inquiry to increase our understanding of the relationship between school counselors’ years of experience and their feelings of burnout.
Organizational Factors Relating to School Counselors Levels of Burnout
Eight organizational factors appear to correlate with school counselors’ levels of burnout, including (a) workplace (Butler & Constantine, 2005), (b) non-counseling duties such as administrative and clerical tasks (Bardhoshi et al., 2014; Moyer, 2011), (c) caseloads (Bardhoshi et al., 2014), (d) AYP (Bardhoshi et al., 2014), (e) level of principal support (Bardhoshi et al., 2014), (f) clinical supervision (Moyer, 2011), (g) student-to-counselor ratio (Wilkerson, 2009; Wilkerson & Bellini, 2006), (h) perceived work environment (Wilkerson & Bellini, 2006), and (i) direct student services (Mullen & Gutierrez, 2016). We categorize these organizational factors into two domains: (a) job responsibilities and
(b) work environment factors.
Job responsibilities. Two studies examined the relationship between school counselors’ non-counseling duties and their burnout scores. First, Bardhoshi and colleagues (2014) examined school counselors’ (N = 212) non-counseling duties and identified a significant relationship between three of the CBI subscales: (a) 11% of the variance in Exhaustion was explained (medium effect size; R2 = 0.11); (b) 6% of the variance in NWE was explained (medium effect size; R2 = 0.06); and (c) 8% of the variance in DPL was explained (medium effect size; R2 = 0.08). Taken together, the results identified that school counselors’ non-counseling duties positively predict their burnout scores.
Moyer (2011) examined how school counselors’ (N = 382) non-counseling duties (non-guidance duties) were correlated to their levels of burnout as measured by the CBI. School counselors’ non-counseling duties accounted for 7.3% of the variance in the burnout score (medium effect size; R2 = .073, ß = .27, p < .01). Receiving supervision accounted for additional variance in school counselors’ burnout scores after controlling the variance explained by non-counseling activities (medium effect size; R2 = .09, ß = -.14, p < .01). As a result, school counselors with more non-counseling duties and less clinical supervision had higher burnout scores. The findings identify the importance of clinical supervision to reduce burnout among school counselors, helping them improve their quality of counseling, which in turn may increase their sense of competence in the workplace.
Bain and colleagues (2011) investigated the mental health of school counselors in a rural setting and their percentage of workweek spent on counseling and administrative duties in South Texas (N = 27). Within this sample of school counselors, 89% had experienced feelings of burnout at least sometimes when trying to provide mental health services; specifically, 41% reported feelings of burnout, and 48% sometimes experienced burnout when providing mental health services to their students. School counselors also reported that they spent the greatest amount of time completing administrative duties and the least amount of time providing counseling services. About 48% of the counselors used more than 50% of their time completing administrative duties, such as organizing facts to report to administrators and preparing for assessments of knowledge and skills, and more than 70% of the participants spent less than 50% of their time providing counseling services. The sample size for this study was small; nevertheless, the results identified that approximately 90% of the school counselors experienced some levels of burnout and spent less time providing counseling services to their students and other stakeholders than completing administrative duties.
Finally, Mullen and Gutierrez (2016) investigated the relationship between burnout and direct student services of school counselors (N = 926). The results indicated that burnout negatively contributed to the frequency of direct counseling activities (ß = -.35, p < .001), direct curriculum activities (ß = -.22, p < .001), and percentage of time at work providing direct services to students (ß = -.24, p < .001). The findings suggest that school counselors experiencing feelings of burnout are likely to have lower numbers of direct counseling activities and curriculum activities, and spend less time offering direct services to students.
Work environment factors. School counselors’ levels of burnout may be different depending on the location of their workplace (Butler & Constantine, 2005). Specifically, school counselors working in urban settings scored higher on the EE subscale as compared to counselors in suburban, rural, and other settings (F (3, 529) = 24.66, p < .001). In addition, counselors in urban settings had higher DP subscale scores than those in other environmental settings (F (3, 529) = 13.67, p < .001). The results may relate to unique stressors school counselors in the urban settings face, including their expected proficiency in working with diverse students (Constantine et al., 2001). Overall, school counselors in urban settings were likely to experience greater feelings of burnout than those counselors in other settings, suggesting that more research is warranted to better understand possible contributors to these educators having higher MBI scores.
Factors relating to school counselors’ work correlating with their feelings of burnout include counselors’ caseloads, AYP status, principal support, and non-counseling duties. Specifically, school-related factors for counselors explained the variance of four burnout subscales of the CBI (Bardhoshi et al., 2014): (a) 21% of the variance in Exhaustion scores was explained (large effect size; R2 = 0.21, p < .001); (b) 9% of the variance in Incompetence scores was explained (medium effect size; R2 = 0.09, p < .01); (c) 49% of the variance in NWE scores was explained (large effect size; R2 = 0.49, p < .001); and (d) 17% of the variance in DPL scores was explained (large effect size; R2 = 0.17, p < .001). As a result, both school counselors’ work-related factors, such as caseloads and non-counseling duties, and their school environment (support from school staff and AYP status) correlate to their feelings of burnout. Therefore, providing sufficient support for school counselors, meeting the AYP, and reducing caseloads and non-counseling duties might mitigate feelings of burnout among school counselors.
Student-to-counselor ratio (Wilkerson, 2009) and perceived work environment (e.g., role conflict; Wilkerson & Bellini, 2006) were identified as predictive factors for school counselor burnout. Wilkerson (2009) found that the hierarchical regression models with variables of demographic data (e.g., years of experience), organizational stressors (e.g., counselor–teacher professional relationships), and coping strategies (e.g., task-oriented coping) explained all three subscale scores of the MBI in a sample of school counselors (N = 198): EE (R2 = .49; large effect size), DP (R2 = .27; large effect size), and PA (R2 = 36; large effect size). Similarly, Wilkerson and Bellini (2006) identified that school counselors’ demographic, intrapersonal, and organizational factors accounted for variance in all three MBI subscale scores, including the EE, DP, and PA subscales (45%, 30%, and 42%, respectively; all large effect sizes). The findings from these studies support that environmental factors relate to school counselor burnout.
Identified Themes From Qualitative Studies
One qualitative study and two mixed-methods studies explored themes relating to school counselor burnout and ways to improve their service, which may offer ways to prevent burnout. Bardhoshi and colleagues (2014) examined how school counselors experienced burnout. Specifically, the emergent themes identified for school counselors’ feelings of burnout organized around four areas including (a) lack of time, (b) budgetary constraints, (c) lack of resources, and (d) lack of organizational support. When school counselors were asked about the meaning of performing non-counseling duties, they stated adverse personal and professional effects, the realities of practice, and reframing the duties within the context of the job. One participant described burnout stating, “It means that I am no longer helpful to my students. I feel like I’m extremely tired and overworked and consequently my effectiveness as a school counselor is negatively impacted” (p. 437).
These themes aligned with existing qualitative research examining school counselors’ feelings of burnout (N = 3; Sheffield & Baker, 2005), including (a) important beliefs, (b) burnout feelings, (c) burnout attitude, and (d) lack of collegial support. One of the participants stated, “I didn’t think I was doing any good for anybody . . . I just can’t go on this way” (p. 181). Another participant stated, “You get to the point where it is no longer fun coming to work or when you are just tired [and] don’t want to deal with anyone” (p. 182). Finally, Bain and colleagues (2011) explored helpful ways to better provide mental health services at school with 27 school counselors in rural districts of South Texas. The results identified that having access to more staff and additional education and awareness of mental health services at their school was needed. Overall, these studies identified common themes of school counselors’ need for collegial support and resources, such as a school climate encouraging collaboration, and identifying gaps in the needs and realities of school counselors (Bardhoshi et al., 2014), as well as reducing the amount of stressful, non-counseling–related work they perform.
Occupational Stress
Researchers examined which factors may influence school counselors’ job stress or job satisfaction, including (a) counselors’ perceived match between job expectations and their actual experiences (Culbreth et al., 2005), (b) the amount of resources in their work environment (McCarthy et al., 2010), (c) mattering to others (Rayle, 2006), and (d) role balance ability (Bryant & Constantine, 2006). Perceived match between initial expectations of the job and actual experiences as a school counselor was the most significant predictor of lower role stress demonstrated by each subscale score of the Role Questionnaire (N = 512; Culbreth et al., 2005): role conflict (medium effect size; R2 = .076); role incongruence (large effect size; R2 = .197); and role ambiguity (medium effect size; R2 = .083). School counseling students reported not feeling trained enough because of the significant amount of non-counseling–related duties, which increased their sense of role conflict.
Graduating from a program accredited by the Council for Accreditation of Counseling and Related Educational Programs accounted for 1.2% of the variance in school counselors’ perceived readiness for the job (small effect size; r = .111, p < .05; Culbreth et al., 2005). School counselors’ balance between job demand and resources was another important factor for their job stress. Moreover, McCarthy and colleagues (2010) identified that perceived job stress and work environment in terms of demands and resources were correlated (N = 227; F (2, 206) = 44.77, p < .001). School counselors with resources, such as other counselors in general or as mentors, and support from administrators scored lower on levels of job stress. The effect size for the difference between the demand and the resourced groups was 1.62 (large effect size), and between the demand and balanced groups was 0.70 (large effect size). In other words, school counselors with more work-related resources were likely to experience lower levels of job stress.
Several factors are related to job satisfaction for school counselors. Rayle (2006) investigated the relationship between school counselors’ (N = 388) mattering to others at work scores and job-related stress scores, and their overall job satisfaction scores. The School Counselor Mattering Survey developed for this study included seven items asking participants to rate their perceived mattering to others, including their students, administrators, and the parents and teachers they worked with. School counselors’ mattering to others at work scores and job-related stress scores explained 35% of the variance in their overall job satisfaction (large effect size; ηp² = .62). Specifically, school counselors’ job satisfaction correlated with mattering to others at work scores (large effect size; r = .44, p < .001) and their job-related stress scores (large effect size; r = -.41, p < .001). In addition, school counselors’ mattering to others scores were negatively associated with their job-related stress scores (r = -.54, p < .001; large effect size). The findings suggest that school counselors’ perceived mattering to others at work and job-related stress predict their overall job satisfaction, and mattering to others at work relates to their job-related stress.
In addition, Bryant and Constantine (2006) investigated the relationship between female school counselors’ (N = 133) role balance, job satisfaction, and life satisfaction. After controlling for demographic information (age, years of school counseling experience, and location of school), role balance and job satisfaction scores correlated with their satisfaction with life scores (large effect size; R2 = .41). As a result, school counselors’ multiple role balance ability and job satisfaction scores positively predicted their overall life satisfaction scores. In sum, these findings identified factors related to school counselors’ job satisfaction, including mattering to others at work, job-related stress, and life satisfaction.
Discussion
Because of the dearth of literature examining school counselor burnout or occupational stress, we reviewed 18 investigations based on the inclusion criteria and included articles focusing on the topic that were published between 2000 and 2018 in refereed journals and identified internal and external factors relating to the phenomena. Specific factors were identified relating to school counselor burnout or stress and their environment, including responsibilities not related to counseling, large caseloads, AYP status, and role confusion. The findings suggest the importance of school counselors asserting themselves to focus on mandated tasks (i.e., counseling) in order to experience less burnout. In addition, it is imperative to train school counseling students to understand the reality of practice, such as other job responsibilities and school climates, and inform them on the necessity of counselors advocating for themselves in order to overcome role confusion and avoid large caseloads. Furthermore, several resources were identified to mitigate burnout among school counselors. Clinical supervision from a competent supervisor is essential for school counselors to get support and learn how to intervene with their clients effectively. In addition, peer supervision or consultation from colleagues may benefit school counselors in sharing their difficulties and gaining other professionals’ perspectives (Butler & Constantine, 2005). Task-oriented coping skills which can be learned in the school counseling programs were also related to a reduced level of burnout among school counselors.
Limitations
Our review needs to be interpreted with some caution, as it is limited to the 18 published studies meeting the inclusion criteria. Therefore, additional research investigating school counselor burnout is needed to further our understanding of this significant construct that may influence the services school counselors provide to their stakeholders. In addition, the reviewed studies include methodological limitations (e.g., sample size, self-report data), further supporting the need for increased research examining the construct of burnout in school counseling. Moreover, no research was identified examining interventions to possibly reduce counselor feelings of burnout.
Implications for School Counseling
Although no studies were identified that investigated treatments for school counselor burnout, research from other similar professions may provide insight for developing coping strategies for school counselors addressing their feelings of burnout. Awa, Plaumann, and Walter (2010) reviewed 25 intervention studies for burnout prevention whose participants included employees from diverse occupations. Seventeen out of 25 studies employed person-directed interventions and indicated the positive effects of the interventions, including cognitive behavioral training (Gorter, Eijkman, & Hoogstraten, 2001), psychosocial skill training (Ewers, Bradshaw, McGovern, & Ewers, 2002), and recreational music making (Bittman, Bruhn, Stevens, Westengard, & Umbach, 2003). Two studies used organization-directed interventions, and one of the studies reduced burnout by using cognitive behavioral techniques, management skill training, and social support (Halbesleben, Osburn, & Mumford, 2006). The other six investigations explored the effects of combined (person- and organization-directed) interventions in reducing burnout. The examples of combined interventions to mitigate counselors’ feeling of burnout include professional supervision (Melchior et al., 1996); work schedule reorganization and lectures (Innstrand, Espnes, & Mykletun, 2004); and participatory action research, communication, social support, and coping skills (Le Blanc, Hox, Schaufeli, Taris, & Peeters, 2007). Overall, Awa and colleagues (2010) identified positive impacts of burnout intervention programs, suggesting potential benefits of these treatment programs for school counselors.
In addition, Krasner and colleagues (2009) reported the effectiveness of their continuing medical education program for physicians to reduce burnout, which involves mindfulness, self-awareness, and communication skills. Educating for mindfulness strategies, self-awareness, and communication skills also may be helpful for school counselors. Providing a supportive environment and acknowledging school counselors’ work may help them increase their sense of matter in their workplace. Lacking empirical studies identifying treatment outcomes for burnout in school counselors, research on decreasing the level of school counselor burnout should be examined both deeply and extensively. Furthermore, intervention programs to prevent and intervene with school counselors’ burnout and occupational stress at the individual and organizational levels are warranted. The efforts to prevent burnout may lead to school counselors providing better quality of services, benefitting the counselors and the students they serve.
Our review indicated that school counselors’ responsibilities, such as non-counseling duties and dealing with large caseloads, hindered counselors from maintaining their wellness. Additionally, experiencing role conflict and employing emotion-oriented coping skills increased their feelings of burnout. Therefore, school counselor preparation programs need to incorporate into their curriculum the characteristics of their future work environment that may involve potential risk factors for burnout. Furthermore, developing school counselors’ own strategies and practicing beneficial skills such as task-oriented coping skills may be helpful for them in decreasing their likelihood of experiencing burnout.
Conclusion
Preventing and reducing school counselors’ feelings of burnout is important to ensure counselors’ ability to provide ethical and effective services to their stakeholders. Failure to address work-related stress in school counselors may cause reduced quality of their service and increased counselor attrition from the profession. Although more investigations examining burnout in school counselors are warranted, this manuscript is the first systematic review of burnout in school counseling, offering increased insight into this significant job-related psychological phenomenon.
Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.
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Jun 28, 2018 | Volume 8 - Issue 2
Matthew C. Fullen
As the number of older adults increases, it is important to understand how attitudes toward aging influence society, the aging process, and the counseling profession. Ageism—defined as social stigma associated with old age or older people—has deleterious effects on older adults’ physical health, psychological well-being, and self-perception. In spite of research indicating that the pervasiveness of ageism is growing, there are few studies, whether conceptual or empirical, related to the impact of ageism within the practice of counseling. This article includes an overview of existing literature on the prevalence and impact of ageism, systemic and practitioner-level consequences of ageism, and specific implications for the counseling profession. Discussion of how members of the counseling profession can resist ageism within the contexts of counselor education, gerontological counseling, advocacy, and future research will be addressed.
Keywords: ageism, aging, older adults, gerontological counseling, advocacy
Currently, there are approximately 47.8 million adults age 65 and over living in the United States, and this number is expected to grow to 98 million—or more than one in five Americans—by 2060 (Administration on Aging, 2017). Much of this growth can be attributed to the aging of the boomer generation, the age cohort born between 1946 and 1964. Approximately 10,000 boomers turn 65 every day (Short, 2016). Increases to the average life span also have expanded the number of older Americans, with a person age 65 now living an average of 19.4 additional years, and many living well beyond that age (Administration on Aging, 2017). Nonetheless, many misconceptions remain about the aging process, and recent research demonstrates that the prevalence of ageism is growing (Ng, Allore, Trentalange, Monin, & Levy, 2015). Ageism—defined here as social stigma related to old age or older people (Widrick & Raskin, 2010)—is associated with the lack of mental health services available to older adults (Bartels & Naslund, 2013), and when negative attitudes toward aging are internalized by older adults, significant consequences to health and well-being may occur (Levy, 2009).
Within the counseling literature, there appears to be a lack of research on ageism and its impact on older adulthood. A keyword search of leading counseling journals dating back to 1992 results in a single publication on the topic of ageism within the American Counseling Association’s Journal of Counseling & Development (Saucier, 2004), as well as a single empirical study in Adultspan Journal (McBride & Hays, 2012). Therefore, to elucidate the effects of ageism, as well as its role within the field of professional counseling, this article will provide a review of existing literature on the prevalence of ageism, its consequences among mental health professionals, and the impact of internalized ageism on older adults. The article concludes with recommendations for how counselors, counselor educators, and counseling students can mitigate the effects of ageism and promote positive perceptions of aging.
Prevalence and Impact of Ageism
Prevalence of Ageism
The term “ageism” was first used in the late 1960s to describe discriminatory beliefs or practices that are predicated on the age of a person or group (Butler, 1969). Like racism or sexism, prejudice associated with age is both pernicious and challenging to quantify. Many myths about aging are assumed to be true without additional consideration, leading to a “commonsense reality” about old age or older people that is then perpetuated throughout a society (Angus & Reeve, 2006, p. 141). Moreover, scholars argue that ageism is currently met with less disapproval than racism or sexism (Cuddy & Fiske, 2002; Nelson, 2016; Palmore, 2005), although more recent empirical research is needed to substantiate this hypothesis. Nevertheless, research indicates that views about aging are becoming more negative (Ng et al., 2015). Dominant myths include the notion that older adults are: (a) lonely and depressed; (b) increasingly similar as they grow old; (c) sick, frail, and dependent; (d) cognitively and psychologically impaired; (e) sexless and boring; and (f) unable to learn or change (Thornton, 2002; Whitbourne & Sneed, 2002). These myths persist in spite of research that demonstrates that older adults are heterogeneous, possess many psychosocial resources, frequently have high levels of self-rated and objectively measured health, and mostly do not experience dementia or other forms of cognitive impairment (Whitbourne & Sneed, 2002).
Stereotypes about older adulthood are transmitted throughout society and may lead to detrimental consequences for the health and well-being of older people. For example, media representations of older adults are likely to reinforce negative views about older adulthood. Television shows, movies, and advertising depict older people according to stereotypes about aging—or omit them altogether (North & Fiske, 2012)—and older people who watch more television over the course of their lives tend to view aging in a more negative light (Donlon, Ashman, & Levy, 2005). Ageism is transmitted through social media as well. References to older adults on Facebook are commonly comprised of references to cognitive or physical debilitation, the infantilization of older people, or suggestions that older adults be banned from public activities like driving or shopping (Levy, Chung, Bedford, & Navrazhina, 2014).
Negative stereotypes may lead to age-based discrimination, a phenomenon that experts describe as both “understudied” and “surprisingly pervasive” (North & Fiske, 2012, p. 983). For example, Posthuma and Campion (2009) described several workplace-based stereotypes that exist, in spite of a lack of supporting evidence. These include the notion that older workers have lower levels of ability and motivation, lower productivity, and greater resistance to change. Within the realm of health care, physicians may be less likely to offer particular medical treatments to older patients because of a belief that certain ailments are the inevitable consequences of natural aging (Bowling, 2007). Ageism may result in elder abuse, both within care facilities and among family members; however, it is underreported because of a lack of awareness among health and social service providers (Nelson, 2005).
Negative stereotypes about aging develop in a manner that parallels stereotypes like racism or sexism. Levy’s (2009) stereotype embodiment theory suggests that ageist views may be transmitted culturally and internalized by older adults, leading to significant changes to health and functioning. Older adults are first exposed to negative stereotypes about aging when they are young. As individuals age into older adulthood, their negative beliefs about aging become increasingly salient and self-directed. On the other hand, if an individual is socialized to hold more positive views toward aging, these viewpoints may serve as a buffer against internalized ageism (Levy, 2009).
Furthermore, stereotype embodiment theory (Levy, 2009) suggests that when stereotypes are assimilated from the surrounding culture, they eventually become self-definitions that influence a person’s functioning and health. Stereotype embodiment theory concludes that: (a) stereotypes are internalized throughout the life span; (b) they are likely to operate unconsciously; (c) as views of older age become increasingly relevant to a person’s identity, the age stereotypes become more salient; and (d) self-referential views on aging are developed via pathways that may be both top-down (i.e., societal perspectives are passed on to the individual) and longitudinal (i.e., views on old age begin in childhood).
Cuddy, Norton, and Fiske (2005) argued that groups within a society are often categorized based on two traits—warmth and competence—and the authors found that most participants rated older adults as warm, but incompetent. Contrary to the belief that ageism is only a concern in Western countries, Cuddy et al. reviewed a large-scale international study that included college students in Belgium, Costa Rica, Hong Kong, Japan, and South Korea. Across samples, participants viewed older adults as significantly more warm than competent, non-competitive, and having lower social status. Within their study, this trend persisted even when looking at cultures and countries that are typically described as more collectivist (i.e., Hong Kong, Japan, and South Korea).
Research indicates that ageism is prevalent within environments where older adults receive housing and health care services. In an ethnographic study on the impact of age and illness within a residential care setting, Dobbs et al. (2008) found that some family members, staff, and residents held negative attitudes about aging that resulted in an environment affected by ageism. In their study, examples of negative age bias included neglecting to gather resident input prior to making decisions, using infantilizing speech with older people, and stigmatizing residents because of dementia or physical disability. In a similar study completed within a multi-level care setting, Zimmerman et al. (2016) found that the use of multi-level, stepped care (i.e., adults with differing independence levels residing within the same setting) reinforced stigma related to age and health, with older adults differentiating among themselves based on which levels of care were required.
Impact of Social Forces
Scholars posit a wide range of hypotheses to explain the prevalence of ageism, but two systemic processes—modernization and medicalization—are identified in the literature as the most likely catalysts of negative attitudes toward aging (Cuddy & Fiske, 2002; Ng et al., 2015). In regard to modernization theory, Cuddy and Fiske (2002) explained that views of older adulthood have changed as a result of the shift from an agrarian society to an industrial society. Technological advances, increased literacy rates among young people, and a trend toward urbanization resulted in greater competition between young and old generations, as well as weakened intergenerational social ties between young people and their families of origin. The sum of these social changes led to decreased status for older people, resulting in the “warm, but incompetent” stereotype that is now associated with them (Cuddy et al., 2005).
Relatedly, improvements in health care have extended the life span and increased the ratio of older to younger people. Previous research shows that as the ratio of older adults to younger adults increases, views about older adulthood become increasingly negative (Ng et al., 2015). Given that the number of older people will increase markedly in coming years, it is possible that negative attitudes toward older people will continue to grow unless intervention occurs.
The second major social force described in the literature is the medicalization of aging, which refers to associating old age with a person’s physical health or illness, to the detriment of other aspects of well-being (Ng et al., 2015). The dominance of medical conceptualizations of old age is described as one of the “master narratives” associated with the modern study of aging (Biggs & Powell, 2001, p. 97). Although the causes of medicalization are many and complex, they can be summarized by the shift from viewing old age as a natural part of the life span to the viewpoint that old age, and even death itself, are problems that modern medicine may be able to solve (Ng et al., 2015). Past research indicates that the medicalization of aging predicts negative attitudes toward aging and consequentially leads to “the objectification of older adults as patients rather than as individuals with interesting life experiences” (Ng et al., 2015, p. 2).
Consequences of Ageism
Impact on Older Adults’ Health and Well-Being
There is a substantial body of research indicating that age stereotypes influence older adults’ health and well-being. For instance, older adults’ perceptions of aging are associated with memory performance (Levy, Zonderman, Slade, & Ferrucci, 2011), hearing decline (Levy, Slade, & Gill, 2006), developing Alzheimer’s symptoms (Levy et al., 2016), and dying from respiratory or cardiovascular illnesses (Levy & Myers, 2005). In fact, Levy, Slade, Kunkel, and Kasl (2002) found that even after controlling for age, gender, socioeconomic status, loneliness, and functional health, older adults with more positive self-perceptions of aging lived 7.5 years longer than those with less positive self-perceptions of aging.
Conversely, research indicates that positive perceptions of aging may provide a salutatory effect on health and well-being. Older adults with positive age stereotypes are 44% more likely to fully recover from severe disability compared to those with negative age stereotypes (Levy, Slade, Murphy, & Gill, 2012), and older military veterans who resisted negative age stereotypes had significantly lower rates of mental illness compared to those who fully accepted them (Levy, Pilver, & Pietrzak, 2014). These positive differences were found for suicidal ideation (5.0% vs. 30.1%), anxiety (3.6% vs. 34.9%), and PTSD (2.0% vs. 18.5%), even after controlling for age, combat experience, personality, and physical health. In regard to variables that may influence older adults’ self-perceptions of aging, Fullen, Granello, Richardson, and Granello (in press) found that resilience—the ability to bounce back from adversity—and multidimensional wellness were significant predictors of positive age perception, whereas increased age and decreased physical wellness predicted internalized ageism. Furthermore, resilience appeared to buffer older adults from experiencing internalized ageism as they grew older. However, older adults may not be exposed to interventions to promote resilience and well-being because of ageism’s impact on the availability of mental health services among older adults.
Impact on Mental Health Professionals
The gap between the mental health needs of older adults and the number of mental health professionals with specific training in working with older adults is on the verge of a “crisis” (Institute of Medicine, 2012, p. ix). Scholars provide a variety of explanations to account for this, including systemic factors—such as inadequate funding and a lack of training opportunities within academic programs (Bartels & Naslund, 2013; Gross & Eshbaugh, 2011; Robb, Chen, & Haley, 2002)—and personal factors, including low interest in working with older adults (Tomko, 2008) and therapeutic pessimism (Danzinger & Welfel, 2000; Helmes & Gee, 2003).
Systemic ageism. Although older adults consistently report higher life satisfaction than younger or middle-aged adults (George, 2010), approximately 26% of all Medicare beneficiaries, or more than 13 million Americans, meet the criteria for a mental disorder (Center for Medicare Advocacy, 2013). Yet, mental health services currently account for only 1% of Medicare expenditures (Bartels & Naslund, 2013). Systemic barriers may be partially responsible for the lack of access to mental health services among older adults. For example, inadequate reimbursement rates is cited as one reason for the 19.5% decline in psychiatrists accepting Medicare between 2005–2006 and 2009–2010 (Bishop, Press, Keyhani, & Pincus, 2014). Similarly, Medicare payments to psychologists for psychotherapy decreased by 35% since 2001, after adjusting for inflation (American Psychological Association, 2014). Older adults are currently unable to use Medicare to access services provided by licensed professional counselors (LPCs) or marriage and family therapists (MFTs; Fullen, 2016b). This translates to an estimate of 175,000 mental health professionals who are unavailable to serve as Medicare-eligible providers (American Counseling Association, n.d.). Clients who age into Medicare coverage after working with these professionals face discontinuity of care caused by having to change providers.
Professional training barriers among the helping and health professions also may reflect systemic ageism. Half of the fellowship positions in geriatric medicine and geriatric psychiatry are unfilled each year, and only 4.2% of psychologists focus on geriatric care in clinical practice (Bartels & Naslund, 2013). Institutional barriers that inhibit student interest in careers related to work with older adults include a lack of visibility for multidisciplinary gerontology programs, the absence of gerontological content within textbooks, few faculty who are trained in gerontology, misconceptions about employment opportunities (i.e., the assumption that the only aging sector jobs available are in nursing homes), and a primary focus on the problems associated with old age when later life is discussed within the classroom (Gross & Eshbaugh, 2011).
Within the counseling profession, scholars describe a mixed commitment to gerontological counseling. Going back to 1975, Salisbury (1975) and Blake and Kaplan (1975) described counseling with older adults as an overlooked domain within professional counseling. Twenty years later, Myers (1995) argued that gerontological counseling had evolved from “forgotten and ignored” (p. 143) to a sub-discipline within the profession complete with standards and certification. However, the gerontological counseling specialization that existed between 1992 and 2008 was discontinued in 2009 when only two institutions had applied for accreditation (Bobby, 2013). Perhaps more telling, the 2016 Standards of the Council for Accreditation of Counseling & Related Educational Programs (CACREP) include zero references to the words old, older, older adults, or ageism; only one reference each to the words age and aging; and four references to the phrase life span (CACREP, 2015). Nonetheless, Foster, Kreider, and Waugh (2009) found that many counseling students have interest in topics related to gerontological counseling, including grief counseling (70%), retirement counseling (43%), family counseling with aging parents (64%), and counseling caregivers (55%). The same study found that many respondents were interested in working in a hospice setting (39%), a hospital geriatric unit (29%), a nursing home (25%), private practice with older adults (43%), and a community setting with older adults (45%). However, it is unclear whether students who are interested in working with older adults receive training and employment opportunities within these contexts.
Individual ageism. Research regarding the prevalence of ageism among individual mental health professionals is equivocal. When mental health professionals’ perceptions of clients based on age, gender, and health variables were studied, some researchers found health bias, but not age bias (Robb et al., 2002). Others reported that participants rated older clients as having a greater number of diagnostic problems (Helmes & Gee, 2003) and a worse prognosis than younger clients, in spite of all relevant information being matched across age groups (Danzinger & Welfel, 2000). Helmes and Gee (2003) found large differences in how older people were rated on key therapeutic variables. Older clients were viewed as less able to develop an adequate therapeutic relationship, less appropriate for therapy, and less likely to recover. Respondents in their study also felt less competent in treating older people, and they were less willing to accept older people as clients.
To counteract the potential influence of negative age bias on counseling treatment, McBride and Hays (2012) described the importance of linking work with older adults to multicultural competence. The authors surveyed 360 counselors and counselor trainees and found a significant, negative correlation (r = -.41) between multicultural competence and negative attitude toward aging. Tomko (2008) found that multicultural competence was associated with improved clinical judgment when working with older adults; however, it did not predict global attitudes toward aging. In sum, considerations of both the systemic and individual aspects of ageism have important implications for the counseling profession.
Implications for the Counseling Profession
The rapid growth of the older adult population will impact members of the counseling profession in a variety of ways. Shifting age demographics make it imperative that counselors understand how the pervasiveness of ageism impacts key professional values like diversity, social justice, and client advocacy. Four domains are outlined in which counselors may dedicate their attention to generating positive views of aging. These domains include counselor education, advocacy, research, and counseling practice.
Counteracting Ageism Within Counselor Education
Within counselor training programs, resistance to ageism begins with incorporating discussions about aging and older adulthood into the counselor education curriculum. Therefore, it is important that professional accreditation standards like CACREP adequately reflect the mental health needs of older adults and their families. In its current form, the omission of keywords like aging, older adulthood, and ageism from these standards may send a mixed signal to counselor training programs and their students about social justice and multicultural competencies as they relate to older adults.
Once ageism is identified by a counselor education program as a priority, counselor educators need to develop strategies for incorporating this focus in the existing curriculum. For instance, a life span development course provides ample opportunities to discuss issues such as shifting population demographics, multigenerational families, and how an aging population will impact the counseling profession. Assessing students’ current thoughts about the aging process, including both their own aging and that of family members, may create greater empathy for the needs of older adults. Similarly, when instructing social and cultural diversity courses, counselor educators should consider introducing topics such as ageism and age privilege and juxtaposing these constructs alongside dialogue about diversity and intersectionality (Black & Stone, 2005). Furthermore, when developing practicum or internship sites, counselor educators could make a point of developing placements in which older clients will be served. Identifying potential site supervisors who have experience in working with older adults is an important step, as it ensures that trainees are given adequate opportunities to reflect on their own perspectives on aging, disability, advocacy, and related issues.
Counteracting Ageism Through Advocacy
In regard to advocacy, counselors should resist ageism at national, state, and local levels. At the national level, the omission of counselors as approved Medicare providers limits the availability of mental health services for older adults and reflects the assumption that older adults’ needs are primarily physiological. This issue creates challenges for members of the counseling profession who are interested in providing services across the life span. Mental health advocacy on behalf of older adults includes educating lawmakers about the importance of Medicare reimbursement as a means of creating mental health service access (Fullen, 2016b). Professional organizations continue to support grassroots advocacy, as well as lobbying efforts, to influence Medicare policy on behalf of counselors. In fact, as of this writing there are bills in each chamber of the United States Congress (i.e., S. 1879; H.R. 3032), and a federal advisory group (i.e., the President’s Interdepartmental Serious Mental Illness Coordinating Committee; ISMICC) recently recommended inclusion of counselors within Medicare (National Board for Certified Counselors, n.d.).
At the state and local level, members of the counseling profession should forge partnerships with gerontology professionals. For example, advocacy occurs when professional counselors and counselor educators make connections with members of the local area agency on aging, directors of local assisted living or skilled nursing facilities, or state policymakers who are responsible for budgetary and policy decisions related to aging. These partnerships are mutually beneficial; they provide members of the counseling profession with increased exposure to the diverse needs of older adults in their communities, and they educate local gerontology professionals about the range of mental health services that counselors provide. Additionally, building interprofessional connections may lead to research opportunities that can improve the care received by older adults.
Counteracting Ageism Through Research
In spite of the numerous studies indicating that ageism has detrimental effects on older adults, there are currently very few studies that demonstrate the prevalence and impact of ageism within the counseling profession. For instance, research on in-session dynamics between counselors and much older clients could shed light on the ways in which age is broached in a counseling session. Additionally, research could focus on the benefits of professional counseling for older adult clients, as well as the effectiveness of novel interventions that are grounded in counseling theories or wellness (Fullen & Gorby, 2016; Fullen et al., in press). For instance, the development and validation of a wellness-based approach to counseling older adults might mitigate mental health issues or internalized ageism among older clients (Myers & Sweeney, 2005), and it would serve as additional evidence for the necessity of adding counselors as Medicare providers.
At the institutional level, more research is needed to understand the extent to which counselor training programs address ageism, and in which curricular contexts. It is important to understand which pedagogical strategies are most effective, whether these impacts persist over time, and how well training programs make inroads with local agencies that work with older adults. Research into advocacy efforts related to Medicare reimbursement may also advance the profession. Although Medicare reimbursement is described as a priority for the counseling profession, there is currently little research on counselors’ knowledge about Medicare or participation in Medicare advocacy.
Counteracting Ageism Through Counseling Practice
Finally, it is important to consider how counselors might resist ageism within their counseling practice. Because of the heterogeneity of older adults, counseling services should be tailored to the unique needs of each client. Given that ageism has the potential to influence how older clients are conceptualized by counselors, it is important for counselors to reflect on their own beliefs about aging as well as their assumptions about the ability of older clients to grow and change. Many counselors are not familiar with the wide range of mental health interventions that have been empirically validated with older adults (Myers & Harper, 2004). For example, the SAMHSA-HRSA Center for Integrated Health Solutions (n.d.) provides numerous resources related to providing behavioral health services to older adults. These resources address issues such as evidence-based treatments for late life depression, preventing suicide in older adults, screening for substance misuse, and assessing cognitive functioning.
Given the growing interest in wellness-oriented services for older adults, SAMHSA also provides evidence-based resources related to health promotion and integrated care. Programs that focus on cultivating holistic wellness or resilience are relatively new, but they also may be worth considering as a means of countering ageism within the practice of counseling. Because the wellness approach incorporates multiple dimensions of functioning, older clients who are experiencing deficits in a particular domain (e.g., limited mobility influencing ability to drive) may find that they can use alternative domains as a means of compensating (e.g., greater reliance on social network to carpool to events; Fullen, 2016a). Similarly, discussion of how older clients have used strengths to navigate loss, overcome adversity, and resist ageism in their own lives may prove to be key ingredients in the therapeutic process. Furthermore, incorporating resilience into an older client’s treatment plan may create a buffer against internalized ageism (Fullen et al., in press), as well as an opportunity to highlight older adults’ abilities to adapt in the face of adversity (Fullen & Gorby, 2016).
Conclusion
As the number of older adults grows, members of the counseling profession are increasingly likely to encounter older people who seek to benefit from counseling services. A review of existing research demonstrates that there are numerous causes of ageism, detrimental consequences associated with internalizing negative age stereotypes, and gaps in research related to how the counseling profession should respond. In light of the counseling profession’s commitment to diversity, social justice, and advocacy, it is important to better understand the broad impact of ageism. By combating ageism in the domains of public policy, research, teaching, and direct service with clients, members of the counseling profession have the opportunity to counteract ageism’s deleterious effects and promote more positive perceptions of growing older.
Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest or funding contributions for the development of this manuscript.
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Jun 28, 2018 | Volume 8 - Issue 2
Ryan M. Cook, Laura E. Welfare, Devon E. Romero
Studies from allied professions suggest that intentional nondisclosure in clinical supervision is common; however, the types of intentional nondisclosure and reasons for nondisclosure have yet to be examined in an adequate sample of counselors-in-training (CITs). The current study examined intentional nondisclosure by CITs during their onsite supervision experience. We utilized content analysis to examine examples of intentional nondisclosure. Sixty-six participants provided examples of intentionally withholding information from their supervisors they perceived as significant. The most common types of information withheld were negative reactions to supervisors, general client observations, and clinical mistakes. The most common reasons cited were impression management, perceived unimportance, negative feelings, and supervisor incompetence. We offer implications for both supervisees and supervisors on how they might mitigate intentional nondisclosure; for example, we present strategies to address ineffective or harmful supervision, discuss techniques to openly address intentional nondisclosure, and explore ways to integrate training on best practices in clinical supervision.
Keywords: intentional nondisclosure, counselors-in-training, supervision, content analysis, best practices in clinical supervision
Counselors-in-training (CITs) in programs accredited by the Council for Accreditation of Counseling & Related Educational Programs (CACREP) are required to complete two supervised onsite field experiences (i.e., practicum and internship) in their area of interest (e.g., clinical mental health, school, rehabilitation; CACREP, 2015). The purpose of this onsite field experience is for CITs to learn the roles and responsibilities of being a professional counselor by applying what they learn in their training programs to their work in a counseling setting (CACREP, 2015). Given CITs’ limited clinical experience, onsite supervisors provide weekly supervision to aid CITs in their professional development (Borders et al., 2011; Borders et al., 2014). Although supervision is a unique opportunity, CITs receive problematic mixed messages about the expectations of the supervisory process (Borders, 2009). CITs are encouraged to discuss the topics and concerns that are the most important to their professional growth (Bordin, 1983), but the information shared is then used by their supervisors to evaluate their clinical performance (Bernard & Goodyear, 2014). These evaluations have a definitive impact on CITs’ ability to pass a practicum or internship course or graduate (CACREP, 2015) and subsequently secure employment in the counseling field. Thus, it is not surprising that studies in allied professions (e.g., clinical psychology, counseling psychology, social work) have shown that trainees commonly withhold potentially unflattering information from their supervisors (Hess et al., 2008; Ladany, Hill, Corbett, & Nutt, 1996; Mehr, Ladany, & Caskie, 2010, 2015; Pisani, 2005). While CITs’ concern to maintain a favorable image in the eyes of their supervisor is understandable, withholding information can result in missed learning opportunities for CITs and negatively impact their clients (Hess et al., 2008).
To date, only two studies have examined supervisee intentional nondisclosure in a sample of counselor education students (Cook & Welfare, 2018; Lonn & Juhnke, 2017). However, neither study examined specific examples of the types and reasons of CIT nondisclosure during onsite supervision. Counselors submit to a unique training model, with specific requirements and goals for master’s-level counselors (e.g., CACREP, 2015). CITs enrolled in CACREP-accredited programs can specialize in one of seven tracks: (a) addictions counseling; (b) career counseling; (c) clinical mental health counseling; (d) clinical rehabilitation counseling; (e) college counseling and student affairs; (f) marriage, couple, and family counseling; (g) school counseling; and (h) rehabilitation counseling. As a result, CITs work in diverse settings with a wide variety of responsibilities that are unique to the counseling profession (CACREP, 2015; Lawson, 2016). Without a study focused on CITs’ experiences in onsite supervision, CITs and supervisors must rely on findings from allied professions that may or may not reflect the counseling training model. Thus, in the current study we aimed to examine the types of intentional nondisclosure and the reasons for the nondisclosure during CITs’ supervised onsite field experience.
Supervised Onsite Field Experience in CACREP-Accredited Programs
Given the growing importance of attending a CACREP-accredited program as an educational requirement for professional counselors (Lawson, 2016), we chose to specifically target intentional nondisclosure by CITs enrolled in CACREP-accredited training programs. State licensure boards are encouraging or mandating that those pursuing professional licensure as counselors must have a degree from a CACREP-accredited program (Lawson, 2016). Additionally, as of January 1, 2022, those applying to be National Certified Counselors (NCCs) will need to graduate from a CACREP-accredited program (National Board for Certified Counselors, 2014). Thus, the standards for onsite field experiences outlined in the 2016 CACREP Standards provide clear guidelines for counselor training. Furthermore, the activities during the onsite field experience are designed to mimic those of a professional counselor in the field (CACREP, 2015). Exploring CIT intentional nondisclosure within the CACREP educational structure can help to inform best practices in counselor training.
Intentional Nondisclosure in Clinical Supervision
The supervision process is reliant on CITs to self-identify important information to share with their supervisors (Ladany et al., 1996); however, identifying this important information is not always clear to CITs given the intricacies of the client–counselor relationship (Farber, 2006; Knox, 2015). Farber (2006) suggested that some nondisclosure “is normative and unavoidable in supervision” (p. 181). Yet, there are instances in which CITs purposefully withhold information they know is relevant because of concerns for what could happen if they shared the information with their supervisor (Hess et al., 2008; Yourman & Farber, 1996).
So why would CITs, who are held to the same ethical standards as practicing counselors (American Counseling Association [ACA], 2014), knowingly choose to withhold information that could be harmful to their professional development or their clients’ treatment? During an onsite field experience, CITs learn the day-to-day tasks of being a professional counselor (e.g., establishing rapport, planning treatment, managing paperwork), but they also must meet the demands of their graduate training programs. Most CITs want to perform counselor functions at a high level, if not perfectly (Rønnestad & Skovholt, 2003). Avoiding clinical mistakes is a dubious belief that CITs hold for themselves (Knox, 2015). These high expectations create a reasonable desire to present oneself favorably to their supervisors, even though supervisors know that perfection is impossible (Farber, 2006). Moreover, CITs are told to share information that is most salient to their personal and professional development with their supervisors, but disclosing information that may be potentially unflattering or embarrassing can then be used by supervisors to evaluate performance (Borders, 2009).
Types and Reasons for Intentional Nondisclosure
In a seminal study on intentional nondisclosure, Ladany et al. (1996) investigated the types and reasons for nondisclosure in a sample of clinical and counseling psychology trainees. Participants were asked to identify instances in which they withheld information from their supervisors and then provide a rationale for why they failed to share that information. The authors found that 97.2% of the participants withheld information from their supervisors.
Through categorizing the content of the nondisclosures, Ladany et al. identified 13 types of nondisclosure, providing definitions and examples of each type: (a) negative reactions to supervisor (e.g., unfavorable thoughts or feelings about supervisors or their actions); (b) personal issues (e.g., information about an individual’s personal life that may not be relevant); (c) clinical mistakes (e.g., an error made by a counselor); (d) evaluation concerns (e.g., worry about the supervisor’s evaluation);
(e) general client observations (e.g., reactions about the client or client treatment); (f) negative reactions to client (e.g., unfavorable thoughts or feelings about clients or clients’ actions); (g) countertransference (e.g., seeing oneself as similar to the client); (h) client–counselor attraction issues (e.g., sexual attraction between client and counselor); (i) positive reactions to supervisor (e.g., favorable thoughts or feelings about supervisors or their actions); (j) supervision setting concerns (e.g., concerns about the placement or tasks required at placement); (k) supervisor appearance (e.g., reactions to supervisor’s outward appearance); (l) supervisee–supervisor attraction issues (e.g., sexual attraction between supervisee and supervisor); and (m) positive reactions to client (e.g., favorable thoughts or feelings about clients or their actions).
They also identified 11 reasons for intentional nondisclosure: (a) perceived unimportance (e.g., information not worth discussing with supervisor); (b) too personal (e.g., information about one’s personal life that is private); (c) negative feelings (e.g., embarrassment, shame, anxiety); (d) poor alliance with supervisor (e.g., poor working relationship with supervisor); (e) deference (e.g., inappropriate for a counselor to bring up because of their role as intern or supervisee); (f) impression management (e.g., desire to be perceived favorably by supervisor); (g) supervisor agenda (e.g., supervisor’s views, roles, and beliefs that guide supervisor’s actions or reactions to supervisee); (h) political suicide (e.g., fear that the disclosure will be disruptive in the workplace and lead to the supervisee being unwelcome or unsupported); (i) pointlessness (e.g., addressing the issue would not influence change); (j) supervisor not competent (e.g., supervisor is inaccessible or unfit for supervisory role); and (k) unclear (e.g., researchers unable to read participants’ statements). The most common types of intentional nondisclosure in the study by Ladany et al. (1996) were negative reactions to supervisor, CITs’ personal issues, clinical mistakes, and evaluation concerns, while the most common reasons for the nondisclosures were perceived unimportance, too personal, negative feelings, and a poor alliance with the supervisor.
Subsequent studies, also from allied professions (e.g., social work, clinical psychology), have found similar results in regard to the types and reasons for intentional nondisclosure (Hess et al., 2008; Mehr et al., 2010; Pisani, 2005). Mehr and colleagues (2010) found 84.2% of psychology trainees reported withholding information from their supervisors, and the most common types of nondisclosures were negative perception of supervision, personal life concerns, and negative perception of the supervisor, while the most common reasons for nondisclosure were impression management, deference, and fear of negative consequences. Additionally, Pisani (2005) found the most commonly withheld information for social work trainees included supervisor–supervisee attraction issues, negative reactions to supervisor, and supervision setting concerns. Finally, in a qualitative study, Hess et al. (2008) explored the differences in a single example of intentional nondisclosure based on psychology trainees’ perceptions of the quality of the supervisory relationship—for example, good (i.e., only one instance of a problem in the supervisory relationship) versus problematic supervisory relationships (i.e., ongoing issues in the supervisory relationship). They found that supervisees in both good and problematic supervisory relationships withheld information about client-related issues. However, supervisees in problematic relationships more commonly withheld supervision-related concerns (e.g., negative reactions to supervisor) compared to supervisees in good relationships. The findings described above provide empirical evidence that nondisclosure in allied professions is common.
The Current Study
Although there is evidence that supervisees from allied professions withhold information, there is currently a dearth of literature regarding intentional nondisclosure by CITs in the field of counseling. Cook and Welfare (2018) found that the quality of the supervisory working alliance and supervisee avoidant attachment style predicted supervisee nondisclosure. In a qualitative study, Lonn and Juhnke (2017) examined supervisee nondisclosure in triadic supervision. They found that the supervisee’s perception of their relationships, the presence of a peer, and opportunity to share were important to whether supervisees withheld information. However, these studies failed to examine the types of information being withheld by CITs as well as their reason for withholding information. Considering that professional counselors have a unique training model (CACREP, 2015), professional identity (Lawson, 2016), and code of ethics (ACA, 2014), the purpose of the current study was to examine the types and reasons of intentional nondisclosure by CITs during their supervised onsite internship experience.
Method
We utilized content analysis (Hsieh & Shannon, 2005) to examine the examples of intentional nondisclosures provided by CITs that occurred in supervision with their onsite internship supervisors. Hsieh and Shannon (2005) defined qualitative content analysis as “a research method for the subjective interpretation of the content of text data through the systematic classification process of coding and identifying themes or patterns” (p. 1278). Our analysis was guided by the findings from Ladany et al. (1996), which allowed us to compare the findings from the current study with those from allied professions while also examining how the phenomenon of intentional nondisclosure might present uniquely in the counseling profession (Hsieh & Shannon, 2005). The current study was designed to answer two research questions: (a) What are the types of information that CITs intentionally withhold from their supervisors during their internship’s onsite supervision? and (b) What are the reasons for their nondisclosure?
Research Team
Our research team included three members. The first and third authors served as coders while the second author served as a peer reviewer. The first and second authors are counselor educators at different universities in the Southeast United States, and the third author was a doctoral student at the same institution as the first author. We all have experience as professional counselors, supervisees, supervisors, and researchers; consequently, we have experienced all parts of the nondisclosure cycle. Prior to the analysis process, we discussed how our previous experiences might impact the analysis. Likewise, we intentionally discussed and bracketed potential influences of bias throughout the project. We also employed triangulation (e.g., multiple coders), utilized frequent peer debriefs, and employed a peer reviewer (Creswell, 2013). Our items also were reviewed by four consultants with counseling, supervision, and research experience to minimize bias and maximize clarity.
Recruitment Procedure and Participants
After securing IRB approval, we recruited participants currently enrolled in internship for the current study through the assistance of counselor education faculty at CACREP-accredited institutions. Fifteen counselor educators at 14 institutions offered paper-and-pencil instrument packets to CITs during one of their class periods. As indicated by the key informants, 152 of the 173 CITs present in class on the day the packets were offered agreed to participate in the study. This resulted in an in-class response rate of 87.86%.
Participants were CITs currently enrolled in internship in a CACREP-accredited program and receiving supervision at their internship sites. The age of the participants ranged from 22 to 60 years old (M = 28.13, SD = 7.43, n = 107). Eighty-eight participants identified as female (80%), 17 participants identified as male (15.5%), three participants identified as nonbinary (gender identity not male and not female, 2.7%), and two participants indicated that they did not want to disclose their gender (1.8%). Regarding race, the majority of participants identified as White (non-Hispanic; n = 71, 64.5%), while 23 participants identified as African American (20.9%), four participants identified as Asian/Pacific Islander (3.6%), three participants identified as Hispanic/Latinx (2.7%), three participants identified as multiracial (2.7%), one participant identified as Native American (0.9%), one participant responded “none of the above categories” (0.9%), and four participants responded that they preferred not to disclose (3.6%). Regarding CACREP track, 64 participants were enrolled in a clinical mental health counseling track (58.2%), 32 participants were enrolled in a school counseling track (29.1%), nine were enrolled in a college counseling and students affairs track (8.2%), and five were enrolled in a marriage, couples, and family track (4.5%).
Instrument
The instrument was designed to gather information about participants’ experiences with their current onsite internship supervisors. Two items were the focus of this study: (a) “Describe a time when you decided not to share something you thought was significant with your current onsite internship supervisor” and (b) “What brought you to that decision to not share it with your current onsite internship supervisor?” In addition, the questionnaire included 15 items to collect demographic information about the participants and their current onsite internship supervisors. Of the 152 participants who began participation, 42 participants (27.6%) were removed from the analysis as they did not complete the open-ended questions, resulting in a final sample of 110 participants. We utilized the demographic variables to check for evidence of nonresponse bias using Chi-square tests of independence and independent t-tests. We did not find evidence of response bias when comparing those who answered the open-ended questions and those who did not.
Data Analysis
We analyzed participants’ responses to the open-ended questions utilizing content analysis. We categorized the types of intentional nondisclosure and the reasons for nondisclosure into categories as recommended by Hsieh and Shannon (2005). For our analysis, we utilized the types of nondisclosure and the reasons for nondisclosure originally identified by Ladany et al. (1996). To reiterate, Ladany et al. identified 13 types of intentional nondisclosure and 11 reasons for nondisclosure (1996). Also, as recommended by Hsieh and Shannon (2005), we allowed for new categories to emerge that did not fit within the categories from Ladany et al. The rationale for this approach was two-fold. First, we could best understand the phenomenon of intentional nondisclosure by comparing our findings to that of previous research from allied professions, while also generating new knowledge of how nondisclosure might uniquely manifest in the counseling profession (Lawson, 2016). Second, utilizing previous research provided structure to our coding procedures and informed the researchers’ interpretation of participant responses (Hsieh & Shannon, 2005).
Coding process. The first and third authors coded the responses of 110 participants for (a) whether or not the participant identified an incident of intentional nondisclosure and (b) to categorize the participant responses that indicated intentional nondisclosure by the type and reasons for the nondisclosure. Each response was coded into one category of type of nondisclosure and one category of reason for the nondisclosure. First, the two coders selected 10 participant responses and coded them as a team. Next, the two coders selected an additional 10 participant responses and coded them independently of each other. They then came together to reach a consensus on the categorization of participant responses. The remaining 90 participant responses were coded independently, and the two coders regularly engaged in peer debriefings throughout the process to ensure consistency (Creswell, 2013). After all 110 participant responses were analyzed, the first and third authors met to finalize the categorization of participant responses and to generate names for the new categories that emerged during the analysis (Hsieh & Shannon, 2005). Regarding the categorization of participant responses in terms of the participant-identified incident of intentional nondisclosure, the coders’ agreement was 100%. Regarding the types and reasons for the nondisclosure, the coders initially disagreed on 15 types of intentional nondisclosure and 23 reasons for the nondisclosure. The two coders established consensus through discussion, resulting in an agreement of 100% (Creswell, 2013). Finally, the second author, serving as a peer reviewer, evaluated the entire coding process. She was chosen based on her expertise with supervision delivery (e.g., protocol, practice) and the topic of intentional nondisclosure. She did not recommend any changes to the categorization of participant responses; however, she recommended renaming two of the new categories for the types of nondisclosures that emerged from the data to better reflect the content of participant responses. Eleven types of intentional nondisclosure and 13 reasons emerged from our analysis.
Results
Forty-four (40%) participants reported that they had never withheld something significant from their current onsite internship supervisors, while 66 (60%) reported that they had. Examples of responses coded as never having withheld something significant from their onsite supervisors include “N/A,” “At this time, I have not withheld any information that I felt was significant with my supervisor,” and “I don’t think there has been one.” For the responses that included an example of intentional nondisclosure (n = 66), 11 types of intentional nondisclosure and 13 reasons for withholding information emerged from the data. The types of intentional nondisclosure included eight types of nondisclosure that were from Ladany et al.’s (1996) research on nondisclosure and three new types of intentional nondisclosure that emerged in this data set: (a) CIT professional developmental needs, (b) a peer’s significant issue, and
(c) experiencing sexual harassment. Regarding the reasons for the intentional nondisclosures, 10 reasons mirrored the findings from Ladany et al. and three reasons were unique to the current study: (a) did not want to harm client or confidentiality concerns, (b) consulted with another supervisor, and (c) issue with other professional in supervision setting.
The Types and Reasons for Intentional Nondisclosures
The most common type of intentional nondisclosures identified by the researchers in the current study were negative reactions to supervisor (n = 18, 27.3%), general client observations (n = 16, 24.2%), and clinical mistakes (n = 15, 22.7%). The most common reasons for intentional nondisclosures were impression management (n = 12, 18.2%), perceived unimportant (n = 8, 12.1%), negative feelings, (n = 8, 12.1%), and supervisor not competent (n = 8, 12.1%). Complete results of the coding and category frequencies of the types of nondisclosures are presented in Table 1, and the final coding and category frequencies of the reasons for nondisclosure are presented in Table 2.
| Table 1
Types of Intentional Nondisclosure |
| Type of Intentional Nondisclosure |
n (%) |
Examples |
| Negative Reactions to Supervisor |
18 (27.3%) |
When my supervisor asked if there is anything that is hindering our relationship, I lied and said that there wasn’t anything and the relationship is fine.
I feel that I am not getting feedback about my counseling from my supervisor in the supervision meetings. Instead I am only getting suggestions of how the supervisor would have handled the client.
Made a comment behind my back. My onsite supervisor is new and so I don’t share too much because he’s easily overwhelmed. |
General Client
Observations |
16 (24.2%) |
I gave [clients] more chances to skip/miss an appointment than [my supervisor] would allow so sometimes don’t let her know when people cancel or no show.
When a client disclosed personal family issues; client’s past trauma. |
| Clinical Mistakes |
15 (22.7%) |
I put a client in danger by a lack of knowledge and being new in a position.
Too much self-disclosure in a session; getting behind on case notes/paperwork.
Having a chronically suicidal client and . . . not assessing for SI in a session and feeling as if when assessed it was not done so well. |
Client–Counselor
Attraction Issues |
4 (6.1%) |
I felt attracted to an assessment client.
During a session, a client told me that he liked how I looked in my pants. He then told me that he got excited at the sound of my voice. |
| Countertransference |
3 (4.5%) |
A client reminded me of my late mother.
Early in internship, I had strong countertransference with a client. |
| Supervision Setting Concerns |
3 (4.5%) |
I was concerned if I was going to have to find another site to finish hours.
Frustration with internship duties. |
| Personal Issues |
2 (3.0%) |
I did not tell my supervisor that I chose to cut it off with a potential romantic partner. |
| CIT Developmental Need |
2 (3.0%) |
When I was first starting out I had a hard time letting my supervisor know when I needed something extra from them whether it be time or information. |
| Negative Reactions to Client |
1 (1.5%) |
Anger toward a student. |
| A Peer’s Significant Issue |
1 (1.5%) |
A client wrote a letter to my co-intern about his sexual desires and love for her. |
| Experiencing Sexual Harassment |
1 (1.5%) |
When I felt sexually harassed by a colleague. |
| Note. Not all types of intentional nondisclosure from Ladany et al. (1996) were present in this sample, and three new types emerged: (a) CIT developmental need, (b) a peer’s significant issue, and (c) experiencing sexual harassment.
|
| Table 2
Reasons for Intentional Nondisclosure |
| Reasons |
n (%) |
Examples |
Impression
Management |
13 (19.7%) |
Concerned about evaluations by those who supervise my supervisors.
Fear of looking bad or being perceived as not being a good counselor.
[Supervisor] might pass judgment because I can’t possibly know what I’m talking about being only an intern.
I worried she will think I’m unprofessional or not trust me with future clients. |
| Negative Feelings |
8 (12.1%) |
Poor self-confidence.
Fear of rejection.
Embarrassment, inferiority felt with supervisor. |
Supervisor Not
Competent |
8 (12.1%) |
I see the way she counsels clients and I know she thinks taking time to establish rapport and positive therapeutic relationships is not always necessary.
Everyone in the office says she is burnt-out and I want to be more compassionate. |
Perceived
Unimportant |
8 (12.1%) |
I did not feel it was necessary.
I was running late to class and I didn’t consult with her because she was in a session with a client so I figured I’d tell her the next day. |
| Deference |
6 (9.1%) |
I did not feel like it would be taken well, and that I am only an intern and should not correct her.
Didn’t want to hurt/upset her or burn a professional relationship. |
| Poor Alliance with Supervisor |
5 (7.6%) |
The power differential.
She berated me in supervision to the point of tears. I feel unsafe with her and our clinical styles contrast.
I knew she would make me feel inferior. |
| Supervisor Agenda |
4 (6.1%) |
I thought he would immediately notify people in charge.
Knowing my supervisor would want to tell [client’s] mother. |
| Political Suicide |
4 (6.1%) |
I want to get hired where I’m working and I don’t feel . . . safe during supervision.
It’s a small practice and I have to share a wall with this offender every day. |
Did Not Want to Harm Client or
Confidentiality
Concerns |
4 (6.1%) |
I didn’t want to put client in a bad situation.
That student was not positive of her status and was not in any danger. Revealing her secret at that point would have damaged the relationship.
Confidentiality issues. |
| Too Personal |
3 (4.5%) |
It was too personal.
I didn’t want to talk about my grief. |
| Pointlessness |
1 (1.5%) |
Thought that was between student and personal physician. |
Consulted with
Another
Supervisor |
1 (1.5%) |
Other supervisor suggestions. |
Issues with Other Professionals in
Supervision Setting |
1 (1.5%) |
The teacher expressed frustration. Hopes to prevent future conflict. |
| Note. Not all categories and reasons from Ladany et al. (1996) were present in this sample, and three new reasons emerged: (a) did not want to harm client or confidentiality concerns, (b) consulted with another supervisor, and (c) issues with other professionals in supervision setting. |
Specific Examples of the Types and Reasons for Intentional Nondisclosure
To provide a more complete picture of the phenomenon of intentional nondisclosure (Hsieh & Shannon, 2005), this section is presented to highlight specific examples provided by participants for each type of nondisclosure and the reasons they withheld the information. Our coded reason for the type of intentional nondisclosure is included in parentheses below (e.g., deference, impression management, political suicide).
Negative reactions to supervisor. One participant stated that she did not disclose that her supervisor “was not helpful during a time that I needed her to be” because the participant “did not want to . . . upset her or burn a professional relationship” (deference). Another participant did not tell her supervisor at her school internship that she disapproved of the way the supervisor addressed a student: “I felt she was being too harsh on a student and not considering other factors.” This participant did not want her supervisor to perceive her as “being wrong” (impression management). A participant stated that even though her supervisor sits in on all of her sessions at her internship site, she still withheld that she is not satisfied with the quality of their relationship and did not share how she felt “in the relationship with her.” She added that she did not disclose this information because “I am afraid she’ll be angry and it will damage the relationship we do have” (negative feelings). Finally, for a clinical mental health CIT, even her supervisor directly asking if she had concerns about the supervisory relationship was not enough to encourage her disclosure: “When my supervisor asked if there is anything that is hindering our relationships I lied and said that there wasn’t anything and the relationship is fine.” The CIT stated she lied because “the power differential, being videotaped, and concerns with confidentiality . . . stopped me from being completely honest about my comfort with our relationship” (poor alliance with supervisor).
General client observations. General client observations differed from clinical mistakes because participants did not self-identify that they perceived the specific examples they provided to be mistakes. Rather, participants indicated that the examples they provided were relevant; however, they failed to disclose this significant information to their supervisors. One school counseling CIT stated that she did not share with her supervisor that she was having trouble “breaking the ice with a client” because she “knew my [supervisor] would make me feel inferior” (poor alliance with supervisor). Another school counseling CIT shared that she failed to disclose that one of her clients was “drinking alcohol on campus” because she thought her supervisor would “immediately notify people in charge of discipline rather than talking to the student first” (supervisor agenda). Finally, another school counseling CIT stated that a client told her she was pregnant, but she failed to notify her supervisor because “that student was not positive of her status and was not in any danger. Revealing her secret at that point would have damaged the relationship” (did not want to harm client; confidentiality concerns).
Clinical mistakes. Participants reported a range of clinical mistakes, from minor clerical errors to potentially more problematic mistakes such as failure to assess for client risk. One clinical mental health CIT did not share that she was “behind on my case notes” because she “did not feel it was necessary” and she “caught up quickly” (perceived unimportant). A student affairs CIT stated that he did not let his supervisor know that he “lacked confidence in theories” because he felt “inadequate” and “embarrassed” (negative feelings). A clinical mental health CIT shared that she failed to disclose something in supervision that her supervisor had previously told her not to do: “My supervisor had previously verbalized that she would be upset.” She withheld this information because “I didn’t want to seem . . . incompetent and I respected her and want her to think I’m doing my best” (impression management). Multiple participants provided specific examples of intentional nondisclosures related to failing to adequately assess for client risk or failing to notify their supervisors that a client was engaging in risk-related behavior. A school counseling CIT shared that she did not discuss with her supervisor that “a client (minor on a school campus) was engaging in [non-suicidal self-injury] again” because “we discussed before how she is obligated to pass that info to school principal who tells parents” (supervisor agenda). This participant added that she decided not to share this information with her supervisor because she perceived the self-injury to be non–life threatening and she wanted to “save rapport” with the client (did not want to harm client; confidentiality concerns). Finally, a school counseling CIT stated that she withheld from her supervisor that she “put a client in danger by my lack of knowledge and being new in my position.” This CIT did not discuss this with her supervisor because “my supervisor wasn’t available” (supervisor not competent).
Client–counselor attraction issues. One clinical mental health counseling CIT stated that her client “told me that he liked how I looked in my pants. He then told me that he got excited at the sound of my voice.” She stated that she did not disclose this information to her supervisor because “I told myself that I did not understand how he meant the comment and I thought he would stop the flirting if I ignored him” (perceived unimportant). Two participants indicated that they experienced sexual attraction to a client but failed to share it with their supervisor. One student affairs CIT stated that she felt “embarrassed” (negative feelings), while a clinical mental health counseling CIT shared that he “did not want anyone to find out and I felt like I handled it fine” (impression management).
Countertransference. One marriage, couples, and family CIT stated that she did not disclose to her supervisor that a client “reminded me of [my] late mother” because she “did not want to talk about [my] grief” (too personal). A clinical mental health counseling CIT echoed the previous participant’s thinking process. She stated she did not tell her supervisor she was experiencing “countertransference” with a client because “it was too personal” (too personal). Finally, another marriage, couples, and family CIT stated that early in her internship she had “strong countertransference with a client” as a result of a personal grieving process. She shared that she did not tell her supervisor because she wasn’t sure “how much I trusted her with this information as it was only several weeks into internship” (poor alliance with supervisor).
Supervision setting concerns. A clinical mental health counseling CIT stated that she did not express her “frustration with internship duties” to her supervisor because “he was unavailable” (supervisor not competent). Another clinical mental health counseling CIT was concerned that she “would need to find another site to finish [internship] hours,” but did not tell her supervisor because “I did not choose to add to stress [of my] site supervisor by posing my concern” (deference).
Personal issues. One participant enrolled in a clinical mental health counseling program withheld from the supervisor that “sad and depressed” feelings because of a “fear of rejection” (negative feelings) arose during supervision. A school counseling CIT did not disclose to her supervisor that she had recently ended a relationship “with a potential romantic partner” even though it was causing her to “feel drained and emotional during the day at her internship” because “I felt that it would be silly to and I thought I did a good enough job ignoring the feelings while with students” (too personal).
CIT developmental need. One clinical mental health counseling CIT shared that she had a difficult time “letting my supervisor know when I needed something extra from them whether it be time or information” because she “felt nervous about [her] position as ‘just an intern’” (negative feelings). Another clinical mental health counseling CIT stated that she failed to let her supervisor know that she is “concerned about being in an individual session with a male client” because she is fearful that her supervisor would think she is “unprofessional or not trust me with future clients” (impression management).
Negative reactions to client. Only one participant indicated that she failed to disclose a negative reaction to a client with her supervisor. This student affairs CIT stated that she did not disclose her “anger towards a client” because she “did not think it was important enough to share” (perceived unimportant).
A peer’s significant issue. One clinical mental health counseling CIT noted that there was a failure to disclose to the supervisor that “a client wrote a letter to my co-intern about his sexual desires and love for her.” This CIT stated that the co-intern did not want this information shared and that the participant “did not think it was my place” (deference).
Experiencing sexual harassment. A clinical mental health counseling CIT stated that she was “sexually harassed by a colleague,” but failed to disclose to her supervisor because “it’s a small practice and I have to share space with this offender every day” (political suicide).
Discussion
The current investigation was designed to examine the types of and reasons for intentional nondisclosure by CITs during their onsite supervision. Sixty percent of the participants provided an example of withholding something significant from their onsite internship supervisors, suggesting that, similar to allied professions, intentional nondisclosure by counseling CITs is common (Ladany et al., 1996; Pisani, 2005; Yourman & Farber, 1996). Participants also provided detailed examples of the types of intentional nondisclosures as well as the reasons they withheld the information. These findings provide insight into the experiences of CITs at their internship placement. In this section, we will connect our findings to those from previous research as well as offer implications for counselors, supervisors, and counselor training programs.
The Types of Intentional Nondisclosure and Reasons for Nondisclosure
Overall, the types of intentional nondisclosure and the reasons for these nondisclosures are comparable to the findings of previous studies in allied professions. There were four categories of the types of intentional nondisclosure that emerged in the study by Ladany et al. (1996) that were not present in the current study: (a) positive reactions to supervisor, (b) supervisor appearance, (c) supervisee–supervisor attraction issues, and (d) positive reactions to client. The category of “unclear” in regard to the reasons for nondisclosure also was not found in the current study, as all participant responses in the current study were legible. Participants of differing CACREP tracks all provided examples of intentional nondisclosure to their supervisors in regard to their field placement. These findings suggest that despite the differences in training models (CACREP, 2015) and professional identities (Lawson, 2016), CITs experience many of the same situations that result in intentional nondisclosure as those from allied professions. The most commonly withheld information in the current study was negative reactions to supervisor, which also was true for psychology trainees in the study by Ladany et al. Supervisees appear most hesitant to discuss their concerns about their supervisor or supervision experience (Hess et al., 2008; Mehr et al., 2010; Pisani, 2005). In addition, CITs also commonly withheld general observations about clients and clinical mistakes similar to allied professions (Hess et al., 2008; Ladany et al., 1996; Mehr et al., 2010; Pisani, 2005).
The CITs in the current study provided many reasons for their intentional nondisclosure, but some reasons were more commonly reported than others. Like the findings from Mehr et al. (2010), participants in the current study most commonly withheld information in order to make a favorable impression on their supervisors. Others reported they withheld because of negative feelings such as “shame” or “embarrassment.” Farber (2006) suggested that internalized negative feelings are often a reason for nondisclosure. Consistent with findings from allied professions (Hess et al., 2008; Ladany et al., 1996), CITs also withheld because (a) they believed a supervisor was not competent, (b) they believed information was not quite important enough to disclose, and (c) they wanted to perform perfectly in their new roles.
Novel Findings Regarding Types and Reasons for Intentional Nondisclosure
An important aspect of content analysis is discussing findings that may extend existing knowledge of a given phenomenon (Hsieh & Shannon, 2005). The current study is the first to examine the types of intentional nondisclosure and reasons for nondisclosure in a sample of CITs. As such, there are several novel findings that warrant discussion. For example, two participants indicated that they did not discuss their professional development needs with their onsite supervisor. This is particularly interesting, given a central function of clinical supervision is to facilitate CIT professional development (Bernard & Goodyear, 2014). CITs who internalize their professional developmental needs as a flaw or who desire to hide these needs for fear of their supervisors’ reactions also may desire to perform perfectly (Rønnestad & Skovholt, 2003). Discussing opportunities for growth as a CIT can be difficult (Mehr et al., 2010); thus, supervisors may need to prompt their supervisees to discuss their needs more directly.
Another novel finding is that one participant indicated that she withheld from her supervisor about her peer’s ethical dilemma (the client letter revealing romantic interest). This participant explained that she did not feel it was her place to share her peer’s information, but all counselors and CITs share some responsibility to address ethical concerns. Ladany et al. (1996) found that 53% of those who withheld information from their supervisors told a peer in the field about their concern. Therefore, it seems likely that other CITs may be placed in a similar position as the participant in the current study. Knowing one’s ethical responsibility to disclose unethical behavior, as in the situation germane to this study, could be prudent (ACA, 2014). Finally, one participant indicated that she was being sexually harassed by a colleague. This report of intentional nondisclosure is particularly concerning given the increased attention to Title IX and attempts to mitigate sexual harassment and sexual assault in university and workplace settings (Welfare, Wagstaff, & Haynes, 2017). This participant’s willingness to share her trauma through the data collection process in this study presents an opportunity for counselor educators and supervisors to explore strategies to prevent these experiences for future CITs.
Regarding the reasons for intentional nondisclosure, there also were novel findings because three new reasons emerged in the current study. First, five participants did not disclose information to their supervisor because they did not want to harm their clients or violate a client’s confidentiality. However, the sharing of information with a supervisor would never violate client confidentiality (ACA, 2014). Perhaps the supervisees’ confusion about the parameters of confidentiality or misdirected efforts to protect clients from the actions of a supervisor they perceived as incompetent led to this decision. A second novel reason for intentional nondisclosure was evidenced by one participant who reported consulting with a supervisor who was not her site supervisor. Ladany et al. (1996) found that 15% of psychology trainees consulted with “another supervisor” outside their primary supervisor (p. 16). Ladany et al. did not ask their participants to clarify the role of another supervisor; however, this finding is relevant to the current study and the training of CITs. Throughout a CIT’s internship experience, they have two supervisors: one onsite supervisor and one university supervisor (CACREP, 2015). It is unclear if the supervisor with whom the participant discussed their concern was another supervisor at the site or the university supervisor. However, this could be an inherent challenge for CITs to identify who to share information with, particularly if there are issues in one of the two relationships. Finally, one school counseling CIT indicated that she had an issue with a teacher and addressed this issue with the teacher directly. Counselors work in diverse settings (ACA, 2014; CACREP, 2015) and may often work with persons outside the counseling profession. Counseling programs and supervisors may need to better prepare students to work with other professionals in their specific setting.
Implications for CITs
The findings from the current study provide empirical evidence that, when faced with the decision to share in clinical supervision, CITs sometimes chose to withhold information from their supervisors despite knowing its relevance. CITs of all CACREP tracks will likely be faced with this difficult decision. We hope that these findings, which offer insights into the experience of intentional nondisclosure, help to normalize the challenges that CITs face and identify strategies to prevent nondisclosure.
Some of the participants described harmful supervision experiences in which they were berated by their supervisors, feared fallout if they were to disclose illegal sexual harassment by another site employee, were concerned about a supervisor’s clinical competence, or did not feel safe to share even blatantly inappropriate client behaviors. Harmful supervision such as this has also been described by Ellis et al. (2014) and is a major concern for counseling and related professions. CITs who find themselves in harmful supervision situations can consider seeking support from another professional, a peer, or a professional association ethics consultant who might help rectify these issues.
Even for those CITs who are not enduring harmful supervision, there are costs to nondisclosure such as stalled development, safety concerns, and ethical or legal violations. Ultimately, the decision to withhold information from one’s clinical supervisor rests with the CIT (Murphy & Wright, 2005). Advocating for a safe and productive supervisory experience may result in a change that serves as a catalyst for supervisee growth or prevents client harm. No supervisee needs to be concerned about burdening a supervisor with disclosures about training issues or ethics; it is the supervisor’s responsibility to address supervisee needs, no matter how burdensome. Relatedly, supervisees who are reluctant to discuss their observations of clients or clinical mistakes for fear of being evaluated poorly or perceived as unqualified should consider ways to demonstrate quality work in order to balance the areas for growth. Making mistakes is expected for all CITs, but it is important to use supervision to learn from these mistakes (Pearson, 2001). In fact, reflecting on previous experiences—and learning from those experiences—is key to becoming a skilled and seasoned counselor (Rønnestad & Skovholt, 2003). CITs also might find it helpful to pursue their own personal counseling as another strategy to facilitate personal and professional growth (Oden, Miner-Holden, & Balkin, 2009).
Several CITs shared their hesitancy in disclosing information to their supervisor for fear of violating their clients’ confidentiality or harming the therapeutic alliance. Although client confidentially is critical, disclosing information to one’s supervisor would not violate a client’s confidentiality (ACA, 2014). In fact, some of the concerns expressed seemed to be more about the limits of confidentiality in the setting more broadly (e.g., high school rules), rather than with the supervisor specifically. Counselors are encouraged to not tell a client that the information shared during the counseling process will remain absolutely confidential. Rather, counselors are encouraged to include a passage in their informed consent about the boundaries of client confidentiality and discuss this information with their clients (ACA, 2014). Finally, predicting when ethical or legal issues will occur may be impossible. Counselors should regularly consult with supervisors to discuss treatment options and legal and ethical issues (ACA, 2014).
Implications for Supervisors and Counselor Education Training Programs
Supervisors and counselor educators play a central role in reducing CIT intentional nondisclosures. The findings from the current study suggest there is a wide range of topics that CITs are reluctant to discuss with their supervisors and a wide range of reasons for withholding. The varying nature of intentional nondisclosures highlights the necessity of individualized interventions. Broadly speaking, supervisors are encouraged to facilitate an open and safe environment that invites disclosure (Bordin, 1983). This might also mean supervisors must be willing to purposefully solicit feedback from their supervisees (Murphy & Wright, 2005). Additionally, supervisors must be proactive in utilizing the knowledge gained from studies like this one to normalize the experiences of their supervisees. Perhaps by discussing each of the types of nondisclosure described above with CITs, supervisors can reduce the pressures associated with performing perfectly (Rønnestad & Skovholt, 2003) or diminish the negative emotions (e.g., shame, embarrassment) associated with making mistakes (Farber, 2006; Knox, 2015).
Finally, some of the experiences described by the participants in the current study are deeply troubling, as they shared specific examples of ineffective and harmful supervision. The burden of providing evidence and reporting instances of harmful supervision is often placed on the CIT (Ellis, Taylor, Corp, Hutman, & Kangos, 2017). We outlined some strategies for CITs in case they were to experience harmful supervision; however, the findings from the current study suggest that CITs are withholding this information for any number of reasons. The participants in this study are not unlike those from other allied professions who have similar supervision experiences (for specific examples of harmful supervision, see Ellis, 2017). Thus, supervisors and counselor education programs must work to prevent CITs from experiencing the damaging effects of ineffective or harmful supervision. We encourage counselor education programs to be proactive by reviewing the signs of ineffective and harmful supervision practices with students before they begin their internships and to regularly check in with students about the supervision experience. Counselor education programs may find it beneficial to solicit student feedback about their practicum/internship site at the end of each term—specifically targeting concerns related to ineffective and harmful supervision.
Encouraging students to disclose their experiences with ineffective or harmful supervision while they are in the process of graded program work might not be possible because of the reasons described above; however, preventing similar experiences for future students may be. Finally, CACREP (2015) requires that all site supervisors receive supervision training prior to serving in this capacity. Accidental instances of ineffective or harmful supervision may be prevented by adding training for site supervisors in this content area (Ellis et al., 2017).
Limitations and Future Research
The current study has limitations that create opportunities for future research. First, we utilized the categories originally identified in the study conducted by Ladany et al. (1996). Although we allowed for the creation of new categories, it is possible that selecting a different study to guide our investigation would have yielded different findings (Hsieh & Shannon, 2005). Also, prompting for a single example of significant intentional nondisclosure may have influenced the findings. Future studies should include the opportunity to provide multiple examples, which could result in different findings. Finally, participants were asked to provide examples of intentional nondisclosure with their onsite supervisors during their internship. These participants were receiving supervision from a university supervisor (CACREP, 2015), meaning the information withheld from the onsite supervisor may have been discussed with the university supervisor. It is also plausible that supervisees withheld the information from both the onsite and university supervisors. Site supervisors and university supervisors might have conflicting agendas, presenting a burden on supervisees to decide what to disclose to whom. Future studies should examine how supervisees decide what to disclose when they have multiple supervisors at one time. Finally, participants in the current study reported they were most hesitant to disclose their negative reactions about their supervisors. Future research should explore how supervisors can better monitor their supervisees’ reactions to them.
Conclusion
Although previous research from allied professions provides evidence of how nondisclosure manifests within those professions, the findings from this study provide empirical evidence of how CIT intentional nondisclosure presents during onsite supervision. These findings provide valuable insights into the types of information that CITs withhold as well as the reasons for their nondisclosure during their onsite supervision. Given that the counseling profession has a unique training model (CACREP, 2015) and professional identity (Lawson, 2016), these findings can be used by CITs, onsite supervisors, and counselor educators to generate targeted solutions to address this critical issue.
Conflict of Interest and Funding Disclosure
This research was supported by a grant from the Association for Counselor Education and Supervision.
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Ryan M. Cook is an assistant professor at The University of Alabama. Laura E. Welfare, NCC, is an associate professor at Virginia Tech. Devon E. Romero, NCC, is an assistant professor at The University of Texas at San Antonio. Correspondence can be addressed to Ryan Cook, 310A Graves Hall, The University of Alabama, Tuscaloosa, AL 35487, rmcook@ua.edu.
