University Student Well-Being During COVID-19: The Role of Psychological Capital and Coping Strategies

Priscilla Rose Prasath, Peter C. Mather, Christine Suniti Bhat, Justine K. James


This study examined the relationships between psychological capital (PsyCap), coping strategies, and well-being among 609 university students using self-report measures. Results revealed that well-being was significantly lower during COVID-19 compared to before the onset of the pandemic. Multiple linear regression analyses indicated that PsyCap predicted well-being, and structural equation modeling demonstrated the mediating role of coping strategies between PsyCap and well-being. Prior to COVID-19, the PsyCap dimensions of optimism and self-efficacy were significant predictors of well-being. During the pandemic, optimism, hope, and resiliency have been significant predictors of well-being. Adaptive coping strategies were also conducive to well-being. Implications and recommendations for psychoeducation and counseling interventions to promote PsyCap and adaptive coping strategies in university students are presented.

Keywords: university students, psychological capital, well-being, coping strategies, COVID-19


In January 2020, the World Health Organization declared the outbreak of a new coronavirus disease, COVID-19, to be a public health emergency of international concern, and the effects continue to be widespread and ongoing. For university students, the pandemic brought about disruptions to life as they knew it. For example, students had to stay home, adapt to online learning, modify internship placements, and/or reconsider graduation plans and jobs. The aim of this study was to understand how the sudden changes and uncertainty resulting from the pandemic affected the well-being of university students during the early period of the pandemic. Specifically, the study addresses coping strategies and psychological capital (PsyCap; F. Luthans et al., 2007) and how they relate to levels of well-being.

University Students and Mental Health
     Although mental health distress has been an issue on college campuses prior to the pandemic (Flatt, 2013; Lipson et al., 2019), COVID-19 has and will continue to magnify this phenomenon. Experts are projecting increases in depression, anxiety, post-traumatic stress disorder, and suicide in the United States (Wan, 2020). Johnson (2020) indicated that 35% of students reported increased anxiety associated with a move from face-to-face to online learning in the spring 2020 semester, matching the early phases of the COVID-19 outbreak. Stress associated with adapting to online learning presented particular challenges for students who did not have adequate internet access in their homes (Hoover, 2020).

Researchers have reported that high levels of technology and social media use are associated with depression and anxiety among adolescents and young adults (Huckins et al., 2020; Primack et al., 2017; Twenge, 2017). Given the current realities of physical distancing, there are fewer opportunities for traditional-age university students attending primarily residential campuses to maintain social connections, resulting in social fragmentation and isolation. Research has demonstrated that this exacerbates existing mental health concerns among university students (Klussman et al., 2020).

The uncertainties arising from COVID-19 have added to anticipatory anxiety regarding the future (Ray, 2019; Witters & Harter, 2020). From the Great Depression to 9/11 and Hurricane Katrina, victims of these life-shattering events have had to deal with their present circumstances and were also left with worries about how life and society would be inexorably altered in the future. University students are dealing with uncertain current realities and futures and may need to bolster their internal resources to face the challenges ahead. In this context, positive coping strategies and PsyCap may be increasingly valuable assets for university students to address the psychological challenges associated with this pandemic and to maintain or enhance their well-being.

Coping Strategies
     Coping is often defined as “efforts to prevent or diminish the threat, harm, and loss, or to reduce associated distress” (Carver & Connor-Smith, 2010, p. 685). There are many ways to categorize coping responses (e.g., engagement coping and disengagement coping, problem-focused coping and emotion-focused coping, accommodative coping and meaning-focused coping, proactive coping). Engagement coping includes problem-focused coping and some forms of emotion-focused coping, such as support seeking, emotion regulation, acceptance, and cognitive restructuring. Disengagement coping includes responses such as avoidance, denial, and wishful thinking, as well as aspects of emotion-focused coping, because it involves an attempt to escape feelings of distress (Carver & Connor-Smith, 2010; de la Fuente et al., 2020). Findings on the effectiveness of problem-focused coping strategies versus emotion-focused coping strategies suggest the effectiveness of the particular strategy is contingent on the context, with controllable issues being better addressed through problem-focused strategies, while emotion-focused strategies are more effective with circumstances that cannot be controlled (Finkelstein-Fox & Park, 2019). In general, problem-focused coping strategies, also known as adaptive coping strategies, include planning, active coping, positive reframing, acceptance, and humor (Carver & Connor-Smith, 2010). Other coping strategies, such as denial, self-blame, distraction, and substance use, are more often associated with negative emotions, such as shame, guilt, lower perception of self-efficacy, and psychological distress, rather than making efforts to remediate them (Billings & Moos, 1984). These strategies can be harmful and unhealthy with regard to effectively coping with stressors. Researchers have recommended coping skills training for university students to modify maladaptive coping strategies and enhance pre-existing adaptive coping styles to optimal levels (Madhyastha et al., 2014).

Flourishing: The PERMA Well-Being Model
     Positive psychologists have asserted that studies of wellness and flourishing are important in understanding adaptive behaviors and the potential for growth from challenging circumstances (Joseph & Linley, 2008; Seligman, 2011). Flourishing (or well-being) is defined as “a dynamic optimal state of psychosocial functioning that arises from functioning well across multiple psychosocial domains” (Butler & Kern, 2016, p. 2). Seligman (2011) proposed a theory of well-being stipulating that well-being was not simply the absence of mental illness (Keyes, 2002), but also the presence of five pillars with the acronym of PERMA (Seligman, 2002, 2011). The first pillar, positive emotion (P), is the affective component comprising the feelings of joy, hope, pleasure, rapture, happiness, and contentment. Next are engagement (E), the act of being highly interested, absorbed, or focused in daily life activities, and relationships (R), the feelings of being cared about by others and authentically and securely connected to others. The final two pillars are meaning (M), a sense of purpose in life that is derived from something greater than oneself, and accomplishment (A), a persistent drive that helps one progress toward personal goals and provides one with a sense of achievement in life. Seligman’s (2011) PERMA model is one of the most highly regarded models of well-being.

Seligman’s multidimensional model integrates both hedonic and eudaimonic views of well-being, and each of the well-being components is seen to have the following three properties: (a) it contributes to well-being, (b) it is pursued for its own sake, and (c) it is defined and measured independently from the other components (Seligman, 2011). Studies show that all five pillars of well-being in the PERMA model are associated with better academic outcomes in students, such as improved college life adjustment, achievement, and overall life satisfaction (Butler & Kern, 2016; DeWitz et al., 2009; Tansey et al., 2018). Additionally, each pillar of PERMA has been shown to be positively associated with physical health, optimal well-being, and life satisfaction and negatively correlated with depression, fatigue, anxiety, perceived stress, loneliness, and negative emotion (Butler & Kern, 2016). At a time of significant stress, promoting the highest human performance and adaptation not only helps with well-being in the midst of the challenge but also can provide a foundation for future potential for optimal well-being (Joseph & Linley, 2008).

Psychological Capital (PsyCap)
     PsyCap is a state-like construct that consists of four dimensions: hope (H), self-efficacy (E), resilience (R), and optimism (O), often referred to by the acronym HERO (F. Luthans et al., 2007). F. Luthans et al. (2007) developed PsyCap from research in positive organizational behavior and positive psychology. PsyCap is defined as an

individual’s positive psychological state of development characterized by (1) having confidence (self-efficacy) to take on and put in the necessary effort to succeed at challenging tasks; (2) making a positive attribution (optimism) about succeeding now and in the future; (3) persevering toward goals and, when necessary, redirecting paths to goals (hope) in order to succeed; and (4) when beset by problems and adversity, sustaining and bouncing back and even beyond (resilience) to attain success. (F. Luthans et al., 2015, p. 2)

Over the past decade, PsyCap has been applied to university student development and mental health. There is robust empirical support suggesting that individuals with higher PsyCap have higher levels of performance (job and academic); satisfaction; engagement; attitudinal, behavioral, and relational outcomes; and physical and psychological health and well-being outcomes. Further, they have negative associations with stress, burnout, negative health outcomes, and undesirable behaviors at the individual, team, and organizational levels (Avey, Reichard, et al., 2011; Newman et al., 2014). Researchers have also examined the mediating role of PsyCap in the relationship between positive emotion and academic performance (Carmona-Halty et al., 2019; Hazan Liran & Miller, 2019; B. C. Luthans et al., 2012; K. W. Luthans et al., 2016); relationships and predictions between PsyCap and mental health in university students (Selvaraj & Bhat, 2018); and relationships between PsyCap, well-being, and coping (Rabenu et al., 2017). 

Aim of the Study and Research Questions
     The aim of the current study was to examine the relationships among well-being in university students before and during the onset of COVID-19 with PsyCap and coping strategies. The following research questions guided our work:

  1. Is there a significant difference in the well-being of university students prior to the onset of COVID-19 (reported retrospectively) and after the onset of COVID-19?
  2. What is the predictive relationship of PsyCap on well-being prior to the onset of COVID-19 and after the onset of COVID-19?
  3. Do coping strategies play a mediating role in the relationship between PsyCap and well-being?


     A total of 806 university students from the United States participated in the study. After cleaning the data, 197 surveys were excluded from the data analyses. Of the final 609 participants, 73.7% (n = 449) identified as female, 22% (n = 139) identified as male, and 4.3% (n = 26) identified as non-binary. The age of participants ranged from 18 to 66 (M = 27.36, SD = 9.9). Regarding race/ethnicity, most participants identified as Caucasian (83.6%, n = 509), while the remaining participants identified as African American (5.3%, n = 32), Hispanic or Latina/o (9.5%, n = 58), American Indian (0.8%, n = 5), Asian (3.6%, n = 22), or Other (2.7%, n = 17). Fifty-four percent of the participants were undergraduate students (n = 326), and the remaining 46% were graduate students (n = 283). The majority of the participants were full time students (82%, n = 498) compared to part-time students (18%, n = 111). Sixty-three percent of the students were employed (n = 384) and the remaining 37% were unemployed (n = 225).

Data Collection Procedures
     After a thorough review of the literature, three standardized measures were identified for use in the study along with a brief survey for demographic information. Instruments utilized in the study measured psychological capital (Psychological Capital Questionnaire [PCQ-12]; Avey, Avolio et al., 2011), coping (Brief COPE; Carver, 1997), and well-being (PERMA-Profiler; Butler & Kern, 2016). Data were collected online in May and June 2020 using Qualtrics after obtaining approval from the IRBs of our respective universities. An invitation to participate, which included a link to an informed consent form and the survey, was distributed to all university students at two large U.S. public institutions in the Midwest and the South via campus-wide electronic mailing lists. The survey link was also distributed via a national counselor education listserv, and it was shared on the authors’ social media platforms. Participants were asked to complete the well-being assessment twice—first, by responding as they recalled their well-being prior to COVID-19, and second, by responding as they reflected on their well-being during the pandemic. 

Demographic Questionnaire
     A brief questionnaire was used to capture participant information. The questionnaire included items related to age, gender, race/ethnicity, relationship status, education classification, and employment status.

Psychological Capital Questionnaire – Short Version (PCQ-12)
     The PCQ-12 (Avey, Avolio et al., 2011), the shortened version of PCQ-24 (F. Luthans et al., 2007), consists of 12 items that measure four HERO dimensions: hope (four items), self-efficacy (three items), resilience (three items), and optimism (two items), together forming the construct of psychological capital (PsyCap). The PCQ-12 utilizes a 6-point Likert scale with response options ranging from strongly disagree to strongly agree. Cronbach’s alpha coefficients as a measure of internal consistency of the HERO subscales in the current study were high—hope (α = .86), self-efficacy (α = .86), resilience (α = .73), and optimism (α = .83)—consistent with the previous studies.

Brief COPE Questionnaire
     Coping strategies were evaluated using the Brief COPE questionnaire (Carver, 1997), which is a short form (28 items) of the original COPE inventory (Carver et al., 1989). The Brief COPE is a multidimensional inventory used to assess the different ways in which people generally respond to stressful situations. This instrument is used widely in studies with university students (e.g., Madhyastha et al., 2014; Miyazaki et al., 2008). Fourteen conceptually differentiable coping strategies are measured by the Brief COPE (Carver, 1997): active coping, planning, using emotional support, using instrumental support, venting, positive reframing, acceptance, denial, self-blame, humor, religion, self-distraction, substance use, and behavioral disengagement. The 14 subscales may be broadly classified into two types of responses—“adaptive” and “problematic” (Carver, 1997, p. 98). Each subscale is measured by two items and is assessed on a 5-point Likert scale. Thus, in general, internal consistency reliability coefficients tend to be relatively smaller (α = .5 to .9).

     The PERMA-Profiler (Butler & Kern, 2016) is a 23-item self-report measure that assesses the level of well-being across five well-being domains (i.e., positive emotion, engagement, relationships, meaning, accomplishment) and additional subscales that measure negative emotion, loneliness, and physical health. Each item is rated on an 11-point scale ranging from never (0) to always (10), or not at all (0) to completely (10). The five pillars of well-being are defined and measured separately but are correlated constructs that together are considered to result in flourishing (Seligman, 2011). A single overall flourishing score provides a global indication of well-being, and at the same time, the domain-specific PERMA scores provide meaningful and practical benefits with regard to the possibility of targeted interventions. The measure demonstrates acceptable reliability, cross-time stability, and evidence for convergent and divergent validity (Butler & Kern, 2016). For the present study, reliability scores were high for four pillars—positive emotion (α = .88), relationships (α = .83), meaning (α = .89), accomplishment (α = .82); high for the subscales of negative emotion (α = .73) and physical health (α = .85); and moderate for the pillar of engagement (α = .65). The overall reliability coefficient of well-being items is very high (α = .94).

Data Analysis Procedure
     The data were screened and analyzed using Statistical Package for the Social Sciences (SPSS, v25). Changes in PERMA elements were calculated by subtracting PERMA scores reported retrospectively by participants before the pandemic from scores reported at the time of data collection during COVID-19, and a repeated-measures ANOVA was conducted to examine the difference. Point-biserial correlation and Pearson product moment correlation coefficients were calculated to examine the relationships of demographic variables, PsyCap, and coping strategies with change in PERMA scores. Multivariate multiple regression was carried out to understand the predictive role of PsyCap on PERMA at two time points (before and during COVID-19). Structural equation modeling in Analysis of Moment Structures (AMOS, v23) software was used to test the mediating role of coping strategies on the relationship between PsyCap and change in PERMA scores. Mediation models were carried out with bootstrapping procedure with a 95% confidence interval.


      Prior to exploring the role of PsyCap and coping strategies on change in well-being due to COVID-19, an initial analysis was conducted to understand the characteristics and relationships of constructs in the study. Correlation analyses (see Table 1) revealed significant and positive correlations between four PsyCap HERO dimensions (i.e., hope, self-efficacy, resilience, and optimism; Avey, Avolio et al., 2011) and the six PERMA elements (i.e., positive emotion, engagement, relationships, meaning, accomplishment, and physical health; Butler & Kern, 2016). Further, PsyCap HERO dimensions were negatively correlated to negative emotion and loneliness. Age was positively correlated with change in PERMA elements, but not gender. Similarly, approach coping strategies such as active coping, positive reframing, and acceptance (Carver, 1997) were resilient strategies to handle pandemic stress whereas using emotional support and planning showed weaker but significant roles. Similarly, religion also tended to be an adaptive coping strategy during the pandemic. Behavioral disengagement and self-blame (Carver, 1997) were found to be the dominant avoidant coping strategies that were adopted by students, which led to a significant decrease in well-being during the pandemic. Overall, as seen in Table 1, all three variables studied—PsyCap HERO dimensions, eight PERMA elements, and coping strategies—were highly related.


Table 1

Relationship of Demographic Factors, Psychological Capital, and Coping Strategies With Change in PERMA Elements

Variables Mean SD P E R M A N H L
Age 27.36 9.91 .15** .11** .14** .16** .14** .01 .03 -.17**
Course Ф .19** .10* .19** .16** .06 -.05 .09* -.14**
Nature of course Ф .06 .06 .12** .13** .09* .03 .03 -.10**
Gender Ф -.01 -.06 .01 -.02 -.02 .02 -.02 .03
Employment Ф -.17** -.11** -.13** -.19** -.10* .04 -.11** .11**
Self-Efficacy 13.80 3.21 .11** .13** .14** .18** .16** -.05 .15** -.03
Hope 18.68 3.92 .24** .26** .20** .34** .40** -.17** .21** -.10*
Resilience 13.41 3.08 .23** .22** .20** .32** .33** -.16** .15** -.13**
Optimism 8.61 2.39 .21** .27** .23** .32** .30** -.11** .16** -.10*
Self-Distraction 6.32 1.41 -.09* .01 .03 -.02 .01 .08* .02 .11**
Active Coping 5.83 2.01 .24** .28** .20** .28** .32** -.09* .23** -.08*
Denial 2.96 1.42 -.19** -.14** -.18** -.16** -.16** .24** -.16** .12**
Substance Use 3.60 2.02 -.18** -.15** -.15** -.20** -.20** .11** -.09* .17**
Using Emotional Support 5.07 1.81 .12** .11** .32** .18** .11** .04 .10* -.02
Using Instrumental Support 4.35 1.70 .01 .04 .20** .07 .02 .10* .04 .07
Behavioral Disengagement 3.96 2.11 -.43** -.37** -.40** -.46** -.44** .31** -.26** .27**
Venting 4.58 1.54 -.24** -.16** -.08* -.17** -.16** .29** -.09* .16**
Positive Reframing 5.12 1.78 .28** .27** .21** .26** .25** -.15** .18** -.14**
Planning 5.42 1.75 .07 .12** .11** .13** .11** .08 .08* -.04
Humor 4.93 2.00 -.02 -.02 -.02 -.04 -.06 -.02 .02 .05
Acceptance 6.47 1.43 .33** .27** .27** .34** .31** -.25** .21** -.15**
Religion 3.93 2.03 .21** .16** .16** .22** .13** -.08 .15** -.05
Self-Blame 4.08 1.72 -.33** -.27** -.29** -.36** -.36** .29** -.22** .20**

Note. P = Positive Emotion, E = Engagement, R = Relationships, M = Meaning, A = Accomplishment, N = Negative Emotion, H = Physical Health, L = Loneliness.
Ф Point-biserial correlation
* p < .05, ** p < .01

Research Question 1
     Results of a repeated-measures ANOVA presented in Figure 1 indicate that mean scores of PERMA decreased significantly during COVID-19: λ = .620; F (5,604) = 73.99, p < .001. Partial eta squared was reported as the measure of effect size. The effect size of the change in well-being for PERMA elements was 38%, ηp2 = .380, a high effect size (Cohen, 1988). As expected, negative emotion and loneliness significantly increased during the period of COVID-19, impacting overall well-being in an adverse manner. The average scores of negative emotion and loneliness increased from 4.46 and 3.86 to 5.85 and 5.94, respectively. Physical health significantly reduced from 6.58 to 5.91. The effect size of the change in the scores of individual PERMA elements ranged between 12.1% and 32.5%. Among the PERMA elements, engagement and physical health were least impacted by COVID-19, whereas students’ experiences of positive emotion and negative emotion were the factors that were largely affected.


Figure 1

Changes in the PERMA Prior to the Onset of COVID-19 and After the Onset of COVID-19

Note. P = Positive Emotion, E = Engagement, R = Relationships, M = Meaning, A = Accomplishment, N = Negative Emotion, H = Physical Health, L = Loneliness.


Research Question 2
     The predictive role of PsyCap on well-being at two time points (before and after the onset of COVID-19) was analyzed using multivariate multiple regression (see Table 2). Coefficients of determination for models predicting well-being from PsyCap dimensions ranged from 4% to 28%. Before the onset of COVID-19, 23% of the variance in well-being was explained by the PsyCap dimensions (R2 = .23, p < .001), with self-efficacy and optimism as the most significant predictors of well-being. However, during the pandemic, the covariance of the PsyCap dimensions with well-being increased to 39% (R2 = .39, p < .01). Interestingly, after the onset of the pandemic, the predictor role of certain PsyCap dimensions shifted. For example, optimism became the strongest predictor of overall well-being and hope emerged as a predictor of engagement, meaning, accomplishment, and physical health during the pandemic. The predictive role of hope was negligible before COVID-19. The predictive role of resilience on positive emotion, accomplishment, negative emotion, and loneliness also became significant during COVID-19. Self-efficacy was a consistent predictor of PERMA elements before COVID-19. But during COVID-19, the relevance of self-efficacy in predicting PERMA elements was limited to controllable factors—relationships, meaning, and physical health—and the predictive role of self-efficacy overall was no longer significant (see Table 2).


Table 2

Predicting PERMA Elements From Psychological Capital Prior to the Onset of COVID-19 and After the Onset of COVID-19

PERMA Self-Efficacy Hope Resilience Optimism Adj. R2 F
Before COVID-19
Positive Emotion .10* -.06 -.01 .44** .19 37.66**
Engagement .10* .06 .01 .11* .05 8.80**
Relationships .10* .07 -.09 .29** .12 21.33**
Meaning .21** .06 -.03 .38** .28 58.68**
Accomplishment .24** .06 .04 .13* .14 25.62**
Negative Emotion -.13** .10 -.05 -.29** .11 18.97**
Physical Health .16** .08 -.04 .12* .07 12.16**
Loneliness -.10* 0 -.01 -.19** .04 7.36**
Well-Being .19** .04 -.02 .35** .23 45.41**
During COVID-19
Positive Emotion .04 .09 .10* .41** .30 67.05**
Engagement .02 .21** .05 .26** .21 40.86**
Relationships .09* .1 -.01 .33** .19 36.72**
Meaning .11** .18** .09 .38** .39 99.93**
Accomplishment .05 .37** .14** .17** .39 96.96**
Negative Emotion -.03 -.07 -.13* -.23** .14 26.80**
Physical Health .16** .19** -.03 .14** .15 27.25**
Loneliness -.04 .01 -.11* -.20** .08 13.34**
Well-Being .07 .22** .08 .37** .39 97.48**

* p < .05, ** p < .01


Research Question 3
     Structural equation modeling was used to examine whether coping strategies mediate PsyCap’s effect on well-being. Coping strategies that predicted change in PERMA were used for mediation analysis. Indirect effects describing pathways from PsyCap factors to PERMA factors through identified coping strategies were tested for mediating roles. Results indicated that PsyCap affected well-being both directly and indirectly through coping strategies. Optimism had a significant indirect effect on change in well-being compared to hope and resilience (see Table 3). Among adaptive coping strategies, active coping, positive reframing, and using emotional support mediated the relationship between optimism and overall well-being. Interestingly, using emotional support also showed a similar mediating link between resilience and PERMA, but not for the factors of loneliness and negative emotion. On the other hand, self-blame and behavioral disengagement were two problematic coping strategies that mediated the relationship between optimism and all PERMA elements. Specifically, we found coping through self-blame playing a mediating role between PERMA factors and two of the HERO dimensions—resilience and hope.


Table 3

Indirect Effect of Psychological Capital on PERMA Factors Through Coping Strategies (Mediators)

PsyCap Standardized Beta (ß, Indirect effect)
                                                                L H N A M R E P
Active Coping Ф
Optimism -.016* .043** -.017* .06** .052** .037** .052** .044**
Resilience -.005 .014 -.006 .02 .017 .012 .017 .015
Hope -.007 .018 -.007 .025 .022 .015 .022 .018
Self-Efficacy -.009 .025 -.01 .034 .03 .021 .03 .025
Positive Reframing Ф
Optimism -.047** .06** -.05** .085** .088** .07** .094** .096**
Resilience -.005 .007 -.006 .01 .01 .008 .011 .011
Hope .003 -.003 .003 -.005 -.005 -.004 -.005 -.005
Self-Efficacy -.005 .006 -.005 .009 .009 .007 .01 .01
Using Emotional Support Ф
Optimism -.007 .02* .012 .03* .049** .086** .029* .032**
Resilience .003 -.012 -.005 -.013* -.021* -.037* -.012* -.014*
Hope 0 0 0 0 0 0 0 0
Self-Efficacy -.001 .006 .002 .006 .01 .018 .006 .007
Self-Blame Ф
Optimism -.038** .043** -.056** .07** .07** .056** .054** .065**
Resilience -.03** .034** -.044** .055** .055** .044** .042** .051**
Hope -.023* .025* -.033* .042* .041* .033* .032* .038**
Self-Efficacy -.005 .006 -.007 .009 .009 .007 .007 .008
Behavioral Disengagement Ф
Optimism -.07** .067** -.081** .113** .118** .104** .097** .112**
Resilience -.02 .02 -.023 .033 .034 .03 .028 .033
Hope -.032 .03 -.036 .051 .053 .047 .044 .051
Self-Efficacy -.009 .009 -.011 .015 .016 .014 .013 .015

Note. Coping strategies with insignificant mediating role are not included in the table. P = Positive Emotion,
E = Engagement, R = Relationships, M = Meaning, A = Accomplishment, N = Negative Emotion, H = Physical Health,
L = Loneliness.
Ф Mediator coping strategies.
* p < .05, ** p < .01



The current study investigated the PERMA model of well-being (Seligman, 2011) with university students before and during the COVID-19 pandemic, as well as the relationships between PsyCap (F. Luthans et al., 2007), coping strategies, and well-being of university students. We examined whether the COVID-19 context shaped the efficacy of particular strategies to promote well-being. Findings are discussed in three areas: reduction in well-being related to COVID-19, shift in predictive roles of PsyCap HERO dimensions, and coping strategies as a mediator.

Reduction in Well-Being Related to COVID-19
     Well-being scores across all PERMA elements, including physical health, were lower than those reported retrospectively prior to the pandemic. Such a decline in well-being following a pandemic is consistent with previous occurrences of public health crises or natural disasters (Deaton, 2012). Participants reported higher levels of negative emotion and loneliness after the onset of COVID-19, and a decrease in positive emotion. It is this balance of positive and negative emotions that contributes to life satisfaction (Diener & Larsen, 1993), and our findings support the notion that fostering particular positive psychological states (PsyCap), as well as engaging in related coping strategies, promotes well-being in the context of this large-scale crisis.

Shift in Predictive Roles of PsyCap HERO Dimensions
     Consistent with prior research (Avey, Reichard et al., 2011; F. Luthans & Youssef-Morgan, 2017; Youssef-Morgan & Luthans, 2015), we found that PsyCap predicted well-being. PsyCap’s positive psychological resources (HERO dimensions) may enable students to have a “positive appraisal of circumstances” (F. Luthans et al., 2007, p. 550) by providing mechanisms for reframing and reinterpreting potentially negative or neutral situations. There was however an interesting shift in the predictive role of PsyCap dimensions before and after the onset of COVID-19. Prior to COVID-19, self-efficacy and optimism were the two major psychological resources that predicted university student well-being. However, after COVID-19, self-efficacy did not present as a predictor of well-being in this study. Although the reason for this result is uncertain, it is conceivable that attending to an uncertain future (i.e., hope) and recovering from immediate losses (i.e., resilience) became more salient, and one’s self-efficacy in managing normal, everyday challenges receded in importance. Indeed, optimism and hope each uniquely predict a major proportion of variance of the change in well-being and may together help students to face an uncertain future (M. W. Gallagher & Lopez, 2009). Resilience, the ability to recover from setbacks when pathways are blocked (Masten, 2001), had a predictive role on positive emotion and accomplishment in this study.

Coping Strategies as a Mediator
     While PsyCap directly relates to well-being and coping strategies relate to well-being, our findings indicated that coping strategies also played a significant mediating role in the relationship between PsyCap and well-being. Specifically, adaptive coping strategies played a significant role in enhancing the positive effects of PsyCap on well-being. Adaptive coping strategies—such as active coping, acceptance, using emotional support, and positive reframing—were found to better aid in predicting well-being. In this study, accepting the realities, using alternative affirmative explanations, seeking social support for meeting emotional needs, and engaging in active problem-focused coping behaviors seem to be the most helpful ways to counter the negative effects of the pandemic on well-being. Conversely, when individuals employed problematic coping strategies such as behavioral disengagement and self-blame (Carver, 1997), the negative impacts were much stronger than the positive effect of adaptive coping strategies.

Implications for Counselors

Given findings of the relationship between PsyCap and well-being in the current study, as well as in prior research (F. Luthans et al., 2006; F. Luthans et al., 2015; McGonigal, 2015), counselors may wish to focus on developing PsyCap to help university students flourish both during the pandemic and in a post-pandemic world. Two significant challenges to counseling professionals on college campuses are the lack of resources to adequately respond to mental health concerns among students and the stigma associated with accessing services (R. P. Gallagher, 2014; Michaels et al., 2015). Thus, efficient interventions that are not likely to trigger stigma responses are helpful in this context. Several researchers have found that relatively short training in PsyCap interventions, including web-based platforms (Dello Russo & Stoykova, 2015; Demerouti et al., 2011; Ertosun et al., 2015; B. C. Luthans et al., 2012, 2013) have been effective. Recently, the use of positive psychology smartphone apps such as Happify and resilience-building video games such as SuperBetter have been suggested and tested as motivational tools, especially with younger adults, to foster sustained and continued engagement with PsyCap development (F. Luthans & Youssef-Morgan, 2017; McGonigal, 2015). These are potential areas of practice for college counselors and counselors serving university students.

Interventions that are described as well-being approaches rather than those that highlight pathologies are less stigmatizing (Hunt & Eisenberg, 2010; Umucu et al., 2020) than traditional deficit-based therapeutic approaches. There are a number of research-based approaches offered in the field of positive psychology to guide mental health professionals to facilitate development of PsyCap and other important well-being correlates. These include approaches to building positive emotions (Fredrickson, 2009); coping strategies, which were found in this study to boost well-being (Jardin et al., 2018; Lyubomirsky, 2008); and effective goal pursuits (F. Luthans & Youssef-Morgan, 2017). One of the distinguishing characteristics of PsyCap is its malleability and openness to change and development (Avey, Reichard et al., 2011; F. Luthans et al., 2006). Thus, there is potential for counselors to develop well-being promotion initiatives for students on university campuses targeting PsyCap and its constituting positive psychological HERO resources with the end goal of strengthening well-being (Avey, Avolio et al., 2011; F. Luthans et al., 2015; F. Luthans & Youssef-Morgan, 2017).

Strategies and programming to develop wellness can be delivered in one-on-one sessions with students, as well as in group settings, and may have either a prevention or intervention focus. They could also be adapted to provide services online. A variety of free online assessments are also available for use by counselors, including tools that measure well-being, positive psychological resources, and character strengths of university students in addition to existing assessment batteries. By administering the PERMA-Profiler to university students, counselors could identify and understand what dimension of well-being should be further developed (Umucu et al., 2020). With each PERMA element individually rendering to flourishing mental health, specific targeted positive psychology interventions might be offered as domain-specific interventions.

Counselors could help university students benefit from attending to, appreciating, and attaining life’s positives (Sin & Lyubomirsky, 2009) and from enhancing the strength and frequency of employing positive coping strategies through targeted psychoeducational or counseling interventions. Teaching university students active coping strategies, such as positive reframing and how to access emotional support, could help them cope with adverse situations. Sheldon and Lyubomirsky (2006) indicated that practicing gratitude helps people to cope with negative situations because it enables them to view such situations through a more positive lens. Among university students, healthy coping strategies could buffer them from some of the unique challenges associated with acculturating and adjusting to college experiences (Jardin et al., 2018), especially during a pandemic.

Limitations and Directions for Future Research

The findings of this study should be considered in light of certain limitations. Foremost among these is that data were collected using self-report measures, and in the case of the PERMA-Profiler, data were collected using the retrospective recall of participants as they considered their well-being prior to the onset of COVID-19. Retrospective recall may be inaccurate (Gilbert, 2007) with participants under- or overestimating their well-being. Given the ongoing repercussions of the pandemic, we recommend continued and longitudinal studies on well-being, coping strategies, and PsyCap. Additionally, data collection methods and sample demographics would likely limit generalizability. We utilized a correlational cross-sectional study design; therefore, although PsyCap was predictive of change in well-being before and during COVID-19, neither causation nor directionality can be assumed. In future, researchers may wish to  investigate whether PsyCap predicts longitudinal changes in well-being in the COVID-19 context.

A further consideration is that the PERMA model of well-being (Seligman, 2011) may not be associated with similar outcomes for people of other cultures and backgrounds during COVID-19. Future researchers examining well-being in university students in different regions of the country or internationally may wish to further investigate the applicability of the PERMA model as a measure of university students’ well-being during the pandemic. Finally, the moderate Cronbach’s alpha reliability scores of < .70 (Field, 2013) for the subscales of the Brief COPE inventory and the engagement subscale of the PERMA-Profiler are of concern, which has also been expressed by prior researchers (Goodman et al., 2018; Iasiello et al., 2017). Future researchers should consider issues of internal consistency as they choose scales and interpret results.


To conclude, the present findings contribute to existing literature on PsyCap and well-being, using the PERMA model of well-being (Seligman, 2011) among university students in the United States in the context of COVID-19. Key findings are that the optimism, hope, and resilience dimensions of PsyCap are significant predictors of well-being, explaining a large amount of variance, with adaptive coping being conducive to flourishing. Further, the present findings highlight the importance of examining the relationships between each element of well-being and with each HERO dimension. Both individual counseling and group-based programming focused on PsyCap and positive coping strategies could support the well-being of university students as they experience ongoing stressors related to the pandemic or as they face other setbacks.


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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Priscilla Rose Prasath, PhD, MBA, LPC (TX), is an assistant professor at the University of Texas at San Antonio. Peter C. Mather, PhD, is a professor and department chair at Ohio University. Christine Suniti Bhat, PhD, LPC, LSC (OH), is a professor and the interim director of the George E. Hill Center for Counseling & Research at Ohio University. Justine K. James, PhD, is an assistant professor at University College in Kerala, India. Correspondence may be addressed to Priscilla Rose Prasath, 501 W. Cesar E. Chavez Boulevard, Durango Building, University of Texas at San Antonio, San Antonio, TX 78207,

In My Own Words: Exploring Definitions of Mental Health in the Rural Southeastern United States

Allison Crowe, Paige Averett, Janeé R. Avent Harris, Loni Crumb, Kerry Littlewood


The following study assessed the utility of the U. S. Department of Health and Human Services’ definition of mental health among participants in the rural Southeastern United States. Using deductive coding, qualitative results revealed that participants do not conceptualize mental health in comprehensive terms. Rather, they tend to describe mental health with a focus on cognition. The sample articulated “well-being” to describe mental health; however, they most often connected it to cognition. The findings suggest that rural communities could benefit from mental health education with a holistic approach and that the use of the term well-being provides a pathway for clinical connections. Future research should consider interviewing rural participants to gather more detail on their definitions and understanding of mental health.


Keywords: mental health, education, cognition, rural, well-being



Mental illness is a pervasive health care concern in the United States. Even though approximately one fifth of adults experience mental health concerns in any year, only 70% of those in need of mental health services seek care (National Alliance on Mental Illness, 2015). Because of how common and widespread mental health conditions are in the United States, mental health professionals have become increasingly aware that educating the public about mental illness is of utmost importance. Mental health literacy (MHL; Jorm, 2012), or the knowledge and beliefs about disorders that assist in the recognition, prevention, or management of a mental health concern, is one way those who are struggling with mental health concerns can manage mental illnesses more effectively. Improving MHL can have the capacity to positively impact negative attitudes, biases, or assumptions that are associated with having a mental illness as well as assist with help-seeking so those who have a mental illness will receive necessary treatment (Crowe, Mullen, & Littlewood, 2018; Jorm, 2012; Kutcher, Wei, & Coniglio, 2016). Researchers have consistently demonstrated that a stigma still exists toward seeking help for mental health concerns and that reducing that stigma is of utmost importance (Kalkbrenner & Neukrug, 2018).


Increasing help-seeking behaviors might best be done through first exploring attitudes and perceptions, as cognitions are closely tied to emotions and behaviors. Therefore, the current study is framed through the theoretical lens of cognitive behavioral therapy (CBT; A. T. Beck, 1970). CBT is based on the notion that how one thinks, feels, and acts are all intertwined. Specifically, one’s thoughts impact how one feels and behaves. Because of this, negative or unrealistic thoughts may contribute to psychological distress. When a person feels distressed, the way that they interpret situations may become skewed or distorted, which then impacts their behavior. From the lens of CBT, one’s decision to not seek treatment for a mental health concern may be closely tied to the thoughts and feelings they hold about negative associations about mental illness. Counselors who practice from a CBT perspective work with clients to identify and eliminate cognitive distortions in order to minimize painful emotions and promote more adaptive behaviors. CBT has been applied to diverse populations and found to be effective with various presenting concerns (A. T. Beck, 1970; J. S. Beck, 2011; Crumb & Haskins, 2017).


Cognitive distortions exist as they relate to having a mental health concern, and researchers have shown that rural residents with mental health concerns fear being negatively labeled, stereotyped, and discriminated against and thus are apprehensive to seek mental health care services (Crumb, Mingo, & Crowe, 2019). Therefore, it is vital that counselors and other mental health providers consider how clients’ thoughts, beliefs, experiences, and other contextual factors contribute to their understanding of mental illness. Intentional acknowledgment of the factors that influence clients’ perceptions, attitudes, and behavior may enhance treatment efficacy for rural residents (Crumb & Haskins, 2017). Along with exploring negative thoughts related to mental health, researchers also have considered MHL, or one’s understanding of mental health, as it impacts behaviors.


Mental Health Literacy (MHL)


Health literacy researchers suggest low health literacy is related to a number of negative health outcomes, including higher instances of chronic illness, lower usage of health care programs, higher costs of health care, and premature death (Baker et al., 2007; Berkman, Sheridan, Donahue, Halpern, & Crotty, 2011). The World Health Organization (WHO; 2013) posits that health literacy is more important than demographic factors (e.g., income, employment, status, education, race, ethnicity) as they relate to health status. Perhaps because of this, the importance of health literacy is well established in the health professions.


In a 2011 literature review on health literacy outcomes, Berkman and colleagues found that lower levels of health literacy were related to more hospitalizations, increased emergency center use, misuse of medications, confusion with medication instructions, higher death rates, and poorer overall health among the elderly. Baker and colleagues (2007) had similar findings related to the impact of poor health literacy on health outcomes. In Baker et al.’s cohort study (N = 3,260), inadequate health literacy independently predicted mortality and death because of cardiovascular disease in elderly populations. They concluded that health literacy is an influential component of overall health.


Compared to general health literacy, the same cannot be said specifically for MHL in the field of mental health. In fact, knowledge of mental health concerns is greatly lacking and largely ignored (Jorm, 2012). The most current study related to MHL found that MHL had a negative relationship to self-stigma of mental health concerns and help-seeking, signifying that when a person knows more information about mental health, they have less stigma about mental health concerns and engage in more help-seeking behaviors (Crowe et al., 2018). In this same study, health outcomes (i.e., blood pressure and body mass index) were assessed to test whether MHL was related to improved physical health. Results were nonsignificant, suggesting that there was not a relationship between MHL and physical health outcomes.


Regional disparities and sociodemographic variations in treatment utilization and efficacy reflect a crucial need for increasing MHL in rural areas in particular (Smalley, Warren, & Rainer, 2012; Snell-Rood et al., 2017). Although prevalence rates of mental health concerns are similar to urban and suburban regions, the amount of and access to mental health services differ vastly in rural regions. Rural residents have fewer options for services, and in fact many rural areas have no health care services at all (Rural Health Information Hub, 2017). Residents in rural regions must travel greater distances for mental health services, are less likely to have health insurance, and have lower MHL (Rural Health Information Hub, 2017). Therefore, professional literature and research studies that assist with raising knowledge about MHL are warranted, as the current literature based on this topic is lacking, especially as it relates to types of settings and samples of the population. Thus, the current study was an attempt to address this gap in the literature. The following section focuses on what is known about mental health in rural areas and highlights the salient issues that are of importance to clinicians and researchers alike.


Mental Health in Rural Areas

The mental health of rural residents is of importance, as 16% of the U.S. population lives in rural areas (Rainer, 2012). Of those living in the rural United States, 90 million residents live in areas that have been designated as Mental Health Professional Shortage Areas and are lacking mental health professionals and resources (Health Resources & Services Administration, 2011). Researchers, practitioners, and recipients of mental health services purport the underutilization of mental health services and inadequacies in the quality of mental health care among rural populations (Smalley et al., 2012; Snell-Rood et al., 2017). Specifically, factors related to acceptability, accessibility, and availability intensify rural mental health disparities across the United States (Office of Rural Health Policy, 2005; Smalley et al., 2012).


A study completed in Australia sought to explore perceptions about mental health in a rural sample (Fuller, Edwards, Procter, & Moss, 2000). Themes revealed a reluctance to acknowledge mental health concerns and seek help from a professional. Results also demonstrated there is a mental health stigma that is particular to rural communities. Although the study provided an initial look at how mental health can be understood in rural areas, the sample consisted of mental health professionals and others who were knowledgeable about mental health issues rather than those from the general client population.


Mental health stigma is one of the most common reasons for unmet mental health needs in rural areas (Alang, 2015; Stewart, Jameson, & Curtin, 2015). For example, residents in rural communities report fear of taking psychotropic medications and that seeking treatment for mental health might adversely impact their employment (Snell-Rood et al., 2017; Stewart et al., 2015). Resultantly, rural clients who experience mental illness enter mental health care later, present with more serious symptoms, and often require more intensive treatment (Smalley et al., 2012). Insufficient MHL, such as misinformation related to common mental health disorders and treatment, can lead to lower rates of recognizing symptoms of depression, anxiety, and an array of other mental health concerns among rural residents in various ethnic and age groups (Kim, Saw, & Zane, 2015).


A quantitative study conducted by Alang (2015) investigating the sociodemographic disparities of unmet health care needs revealed men in rural areas were more likely to forgo mental health care because of gender stereotypes about mental health problems that encourage men to ignore mental health concerns and avoid help-seeking behaviors. Similarly, Snell-Rood et al. (2017) found that rural women face issues with mental health treatment quality and stigma related to specific disorders such as depression as well as a cultural expectancy of self-reliance, which impacts treatment efficacy. Study participants shared that the quality of counseling in their rural settings was unsatisfactory because of counselors recommending coping strategies that were “inconsistent” with their daily routines and beliefs, not offering adequate “direction” on how to approach treatment for their concerns, and having a lack of therapeutic interaction (Snell-Rood et al., 2017). Because of negative perceptions of the quality of mental health treatment, many women in the study were ambivalent in regard to seeking professional help. Rather, they relied on their personal approaches to symptom management (e.g., avoidance, reflection, and prayer).


Accessibility of mental health services is a significant concern in rural areas. Rural residents face challenges in finding transportation to facilities for professional care. Consequently, rural residents often forgo attaining adequate and timely mental health treatment (Alang, 2015; Hastings & Cohn, 2013). Rural residents often depend on alternative sources such as faith-based organizations to address mental health concerns (Bryant, Moore, Willis, & Hadden, 2015) or ignore the prevalence of mental health symptomology altogether (Snell-Rood et al., 2017). Unfortunately, researchers indicated that rural residents seek treatment for mental health disorders after they have become progressively worse, resulting in more extensive treatment, which is often unavailable or costly for rural clients (Gore, Sheppard, Waters, Jackson, & Brubaker, 2016; Hastings & Cohn, 2013; Snell-Rood et al., 2017). Deen and Bridges (2011) suggested these delays in seeking mental health treatment are associated with low MHL.


Treatment availability for mental health care in rural areas is fragmented because of critical shortages in mental health care providers in these communities (El-Amin, Anderson, Leider, Satorius, & Knudson, 2018; Snell-Rood et al., 2017). Practitioner shortage is attributed to difficulty in recruiting and retaining professionals for rural practice as well as practitioners’ limited understanding of cultural norms and effective interventions to address mental health needs in rural communities (Fifield & Oliver, 2016; Hastings & Cohn, 2013). Among practitioners who provide clinical services in rural areas, many report feeling incompetent to work with the population because of receiving fewer training opportunities to learn how to work with rural populations, less access to consultation resources, and professional isolation (Hastings & Cohn, 2013; Jameson & Blank, 2007). Fifield and Oliver (2016) found the most common need of rural-area mental health professionals was training opportunities specific to rural mental health counseling. Pointedly, rural mental health service providers are encouraged to tailor interventions and informational material to meet the needs of the specific communities in which they practice (Crumb, Haskins, & Brown, 2019; El-Amin et al., 2018). For example, a qualitative study examining the experience of rural mental health counselors found it was necessary for rural counselors to modify their interventions to include community-based interventions and expand their roles to include consulting, advocacy, and case management to effectively meet the needs of rural clientele (Crumb, Mingo, & Crowe, 2019). In 2012, rural-specific supplemental materials and curricula were integrated into the standard Mental Health First Aid program, a training course disseminated by the National Council for Behavioral Health to address gaps in MHL by teaching skills to help individuals identify, understand, and respond to mental illness (El-Amin et al., 2018; National Council for Behavioral Health, 2019). Based upon extant research evidence, cultural distinctions in rural living impact MHL and, subsequently, the quality of mental health care in rural regions of the United States.


Despite the above-mentioned disparities, there are opportunities for improving the mental health care of those in underserved rural areas. By becoming familiar with how rural residents in the United States define mental health and investigating the sociodemographic idiosyncrasies in the meaning of mental health for rural residents in specific regions of the United States, mental health practitioners can understand how to better address needs, counter structural barriers to treatment, and improve overall mental health care in rural areas. As far as we are aware, there are no studies that have examined how those in rural communities define and conceptualize mental health. Thus, the current study was designed to fill this gap in the literature.


This study sought to understand how individuals in the rural Southeast define and conceptualize mental health in order to explore MHL and serve as a guidepost to providing culturally relevant services to residents in these regions. Areas in the Southern United States have a high concentration of rural residents who potentially have less access to mental health services, which may influence their overall MHL (El-Amin et al., 2018). Furthermore, we know little about how rural populations define mental health and the knowledge and beliefs that undergird their understanding of mental health. Rather, we have definitions of mental health that are taken from large national and international entities (e.g., U.S. Department of Health and Human Services, Centers for Disease Control and Prevention [CDC], WHO) that offer broad ways of understanding the term. These definitions, although useful, may not capture distinctions associated with region, socioeconomic status, or cultural group differences. Understanding how groups of people view mental health has many benefits to enhancing MHL. A more specific understanding of mental health concepts can serve as a foundation to increase the utilization of mental health services, improve the quality of care, and enhance clients’ ability to communicate concerns. If there are to be greater gains in prevention, intervention, and management of mental health in rural, southern regions of the United States, we need a comprehensive understanding of aspects that are included in perceptions of mental health—using their own words.





Prior to data collection, the Institutional Review Board at a Southeastern U.S. university granted approval to complete the study to explore MHL. Data were collected via a paper-and-pencil survey. Research team members approached patients waiting for a regularly scheduled medical appointment with their primary care physician to complete the survey. Paper copies were stored in a locked filing cabinet within a locked office. The family medical center was located in a rural area of a state in the Southeastern United States. The family medical center where the research took place also housed a mental health provider who received referrals from the medical doctors at the same site. The research team asked permission to collect data on-site, and the lead physician at the center agreed. The mental health provider provides services to many of the same patients who receive medical care at the office. This study was part of a larger, quantitative research investigation on mental health, mental health stigma, and MHL (Crowe et al., 2018). Because of the expansive nature of the dataset, however, this article only focuses on the qualitative components of the survey.



Using published guidelines for in-person recruitment, the research team approached patients as they waited in the waiting room and asked if they would be interested in joining the research study (Felsen, Shaw, Ferrante, Lacroix, & Crabtree, 2010). When participants elected to participate in the study, they completed an informed consent and survey in the waiting area or in an exam room while waiting for the medical professional. All data were collected over the course of approximately six months. Incentives were not offered to participants and all participants could choose to opt out of participation at any time.


Participants included 102 individuals, including 65 females (63.7%) and 37 males (36.3%). A total of 70 participants identified as White (68.6%), 25 identified as Black/African American (24.5%), four identified as multiracial (3.9%), two did not know or endorsed the “other” category (2%), and one identified as Asian (1%). Regarding age, 30 (29%) participants were age 60 and above, 21 (21%) were between the ages of 50–59, another 21 (21%) were between the ages of 40–49, 14 (14%) were between the ages of 30–39, 14 (14%) were between the ages of 19–29, and two (2%) were 18 or younger. Fifty-six participants (55%) were married, while 27 (26%) were single. A total of 15 (15%) were separated/divorced, and four (4%) were widowed. One hundred and twelve participants were asked to complete the survey, and102 individuals completed the materials, yielding a 91% useable response rate. Demographic information is summarized in Table 1.


Table 1

Demographic Information

Characteristic n %
     Male 37 36.3
     Female 65 63.7
     African American/Black 25 24.5
     Caucasian/White 70 68.6
     Multicultural   4   3.9
     Other   2   1.9
     Asian   1   0.9
     18 or younger   2   1.9
     19–29 14 13.7
     30–39 14 13.7
     40–49 21 20.6
     50–59 21 20.6
     60+ 30 29.4
Marital status
     Married 56 54.9
     Single 27 26.5
     Separated/Divorced 15 14.7
     Widowed   4   3.9
Seeking treatment for
     Physical health concerns 88 86.3
     Mental health concerns   3   2.9
     Both   2       2
Treatment status
     Never sought treatment 54 52.9
     Sought treatment in the past 48 47.1
Length of treatment
     1 year or less 18 17.6
     1–4 years 11 10.8
     5–10 years   9   8.8
     11–25 years   5   4.9
Description of treatment
     Not at all helpful   1       1
     Somewhat helpful   9   8.8
     Generally helpful 10   9.8
     Very/Extremely helpful 28 23.3
Family mental health
     No immediate family member with
a mental illness
       66      64.7
     Immediate family member with a
mental illness
26 25.4
     Not sure 10  9.8
Would you seek treatment for mental health concerns in the future?
     Yes         78    76.5
     No   3   2.9
     Not sure 21 20.5





For purposes of the current analysis, an open-ended question prompted participants: “In your own words, please describe what you believe the term mental health refers to.” Analysis was completed by comparing and contrasting the participant responses to this prompt with the U.S. Department of Health and Human Services (HHS; 2019) definition of mental health. This definition states that:


Mental health includes emotional, psychological, and social well-being. It affects how we think,
feel, and act. Mental health helps determine how we handle stress, relate to others, and make
choices. It is important at every stage of life, from childhood and adolescence through adulthood.
. . . Many factors contribute to mental health problems, including: biological factors, such as
genes or brain chemistry; life experiences, such as trauma or abuse; [and] family history of mental
health problems. (para. 1–2)


We elected to use this definition as opposed to similar definitions of mental health offered by WHO or the CDC because each member of the research team chose it as the most comprehensive of the three. Although there were many overlaps in the three definitions (i.e., all three descriptions mention well-being and handling or adjusting to stressors, and included some dimension of biological, psychological, and social aspects), the HHS definition also included the notion of life stages, past life experiences, and how these factors impact mental health.


Data Analysis

The deductive qualitative analysis method (Gilgun, 2011) was used to analyze participant responses to the open-ended prompt. In deductive coding, the researchers begin with existing codes, as deductive coding is utilized to test existing theories or frameworks. Because the current research was attempting to test the HHS definition, deductive analysis was considered the most fitting analysis method by the research team.


Deductive analysis attempts to understand how a particular theory or framework is useful or not (Gilgun, 2011). In deductive coding, data is sorted as it fits with existing concepts, or codes, within a framework. Deductive coding includes levels of analysis, including open coding, axial coding, and selective coding (Strauss & Corbin, 1990). According to Gilgun (2011), during open coding, the data is read line by line, sentence by sentence, and is placed as it is understood within the existing concept(s) with which it best aligns. Axial coding then occurs to refine the existing theory or framework via further analysis of data that is already placed within concepts. This data is then reconsidered to attend to groupings or subthemes within the concept to see if further details of a theory/framework are possible. Selective coding within deductive analysis is when the data is further examined to see if there is possible reduction to a single category or core concept. Selective coding is also an attempt to refine and further consider the existing framework to determine its utility and add to its use. As well, lines or sentences that do not fit existing concepts are noted. This is referred to as negative case analysis.


In the current research, the open coding analysis process was conducted repeatedly to consider and reconsider the data and its fit to the concepts within the HHS (2019) definition of “think, feel, and act.” These three concepts in the HHS definition were evident as the most salient. To aid in coding, these three HHS concepts were further understood by utilizing several online dictionaries (e.g., Google dictionary, Merriam-Webster,, and Cambridge English Dictionary) to define each concept. For example, the think code included all participant responses that are associated with this term via dictionaries, including intellectual, cerebral, brain, cognitive, and rational. The research team continually used several dictionaries to understand participant responses that were not exact or clear upon first reading. For example, state of mind was coded as think because of it being defined as a cognitive process and the condition of a person’s thoughts. Axial coding then occurred through the research team reconsidering the fit of the responses to the existing codes and if further codes could be developed via negative case analysis. As demonstrated below, axial coding produced a negative case analysis, that of overall well-being. Selective coding occurred through the team considering all codes and the utility of the original framework or, in this case, the HHS definition. This utility or lack thereof is further considered in the discussion below.


The entire analysis process was completed by two members of the research team independently. Independent coding enhances credibility in the analysis process, a technique promoted among qualitative researchers (Lincoln & Guba, 1985). The two researchers met on two occasions to discuss their findings and found consistency in their coding in both meetings. This consistency is often found when pre-existing codes with set definitions are utilized, as was the case in this analysis.




The following section presents the results of the deductive coding of the data in comparison to the HHS definition of mental health, specifically the concepts of how we “think, feel, and act.” The existing concepts used as codes for analysis included the psychological, emotional, and social well-being—how we think, feel, and act. Sample quotes from participants (Ps) are provided. The research team also presents further points of possible refinement of the definition and sense of a core concept.


Concepts Used to Describe Mental Health

     Think, feel, and act. Only 15 participant responses provided support for a definition of mental health that encompassed all three aspects found in the HHS (2019) definition of “think, feel, and act.” One participant stated, “mental health to me personally is the state of one’s condition of emotional, mental, social and physical well-being” (P2), and another shared that mental health is the “ability to succeed, fully participate in social, emotional and occupational and recreational leisure” (P3). Thus, there was only a small subset of participants who viewed mental health as comprehensively as federally defined.


     Well-being. It should be noted that although most participants did not provide comprehensive definitions that specifically mentioned all three concepts of think, feel, and act, as used by HHS, there were 23 participants in the sample who used the term well-being. As indicated in the following, well-being was not seen as specific to one area but rather an overall experience. One participant stated, “More than a sense of psychiatric disease—overall well-being” (P4), and another shared, “Overall health of a person—their well-being” (P5). Thus, for many of our participants, a comprehensive definition of mental health they demonstrated was the general term well-being.


     Think. The most salient concept found among our participants was related to cognition, thinking, the mind or brain, or the term mental. Thirty-four responses focused solely on mental health as being how we think, including statements such as “state of mind” (P6), “mental health refers to your thoughts” (P7), and “brain imbalance” (P8). These responses suggest that the cognitive aspect of mental health is a primary way these rural participants conceptualize mental health. We also saw this demonstrated in other definitions provided by the participants that had think in combination with either feel or act.


     Think and feel. The next most salient conceptualization provided by participants included elements of both cognition and emotion—how we think and feel. Eighteen participants provided responses in this code, including “mental health is my ability to cope, how I think, rational thinking, and my emotional stability” (P9) and “state of mind and feeling of well-being” (P10). It is noteworthy that again when discussing cognition and emotion, there was frequent use of the phrase well-being, even when limited to just think and feel, thus further supporting the term well-being.


     Think and act. Cognition or thinking was further salient and used in connection with behavior, or how we think and act. Conceptualizations from these 10 participants included statements such as “condition of one’s mind and if any affect [sic] on behavior” (P11) and “well-being in thought and action” (P12). Again, also noted is the use of the term well-being, even when specifying think and act.


     Non-salient concepts within the HHS definition. There were other conceptualizations of the term mental health that supported aspects of the HHS definition. There were seven participants who focused solely on feelings—how you feel. Although there were five participants who only focused on mental health as behavior or how one acts, neither of the singular concepts were considered salient in participant responses because of infrequent responses.


     Other non-salient concepts. Also provided by participants were concepts focused specifically on a mental health diagnosis such as “depression” (P16, P17) and “depression, bipolar” (P18). Also, it is interesting to note that although not salient, a few participants saw mental health as a function of physical health. This was demonstrated in definitions such as “condition of health” (P19) and “special help for the sick or assist those that have some type of disease” (P20). It is important to note that a few responses were unclear or too vague and could not be categorized, such as “Don’t know” (P21, P22).



     Overall, participants’ responses suggest a strong tendency toward cognitive aspects of mental health rather than a comprehensive definition that can be found when looking in formal sources, such as the HHS definition (2019). However, a negative case that emerged was that these rural participants did provide the term well-being as an overall comprehensive definition for mental health. Frequency counts for each concept can be found in Table 2. In the following section, we discuss these findings.


Table 2

Frequencies According to HHS Definition Code

HHS Definition Codes              Participant Response Count

Think                                                   34

Think and Feel                                      18

Think, Feel, and Act                             15

Think and Act                                      10

Well-Being                                           23






     In the current study, we explored MHL, specifically focusing on the efficacy of the HHS mental health definition in a rural, Southeastern U.S. sample. We sought to understand how this population conceptualized the term mental health. The current research literature provides very little information about this topic, so the following study offered initial findings to offer professional counselors and researchers implications and areas for further investigation.


It is important to reflect on the ways results from this sample of rural residents compare to the existing knowledge about the larger public’s MHL levels and ideas about mental health. Jorm (2000, 2012) noted that lack of MHL among individuals inhibits their ability to recognize mental health concerns when they arise. Although MHL may be an area for more intensive focus across all populations and settings (Jorm, 2000, 2012), results from this study suggest that there are a number of unique considerations in rural areas. Moreover, it is important to situate the current findings in the context of the challenges faced by rural residents. Knowing how those in rural communities define mental health, in their own words, will lend mental health practitioners information about how to communicate and connect effectively to increase the utilization of mental health services, improve the quality of care, and enhance clients’ ability to communicate concerns. If there are to be greater gains in prevention, intervention, and management of mental health in rural regions of the United States, a nuanced understanding of perceptions about mental health may offer a starting point.



In terms of the HHS definition, the current sample supported the concept of well-being. Although well-being was not necessarily connected concretely to the specific terms of think, feel, and act, it was often associated with one or two other concepts as well as used singularly as a holistic definition. Research on well-being has been of increasing interest in the past two decades (Dodge, Daly, Huyton, & Sanders, 2012), and many of the current national and international definitions of mental health refer to well-being (LaPlaca, McNaught, & Knight, 2013). Recent attempts have been made in the literature to more clearly articulate the definition of well-being (Dodge et al., 2012; LaPlaca et al., 2013), as this concept is being used to determine policy and practice on many national stages.


Current definitions of well-being combine elements of the psychological, social, and physical. However, these descriptions also focus on the ratio of resources to challenges that individuals and communities experience, and some have described well-being as the equilibrium between the two (Dodge et al., 2012). Thus, well-being should be considered within the context of social issues, economics, and service provision. This definition of well-being can be particularly useful for rural communities and populations, as resources and service provision are often lacking in rural communities (Health Resources & Services Administration, 2011). Our finding that participants connected with and utilized the concept of well-being suggests that both counseling practitioners and researchers should utilize the term and seek to better understand it, especially those working with rural communities and clients. However, participants in the current study did not provide an expansive level of detail in their conceptualization of well-being; rather, they focused on the cognitive or physical aspect of well-being.


Cognitive and Biological Focus

When comparing the current sample’s definitions to the HHS definition of mental health, the think/cognitive aspect of mental health was most supported and relevant to these rural participants. Most participants believed that mental health describes how individuals think, followed by those who described it as a combination of thoughts and feelings. As noted in the literature review, HHS (2019) considers mental health as impacting the way individuals think, feel, and act. In the current sample, however, only a small fraction of participants defined mental health as a combination of thoughts, feelings, and behaviors. Instead, participants considered mental health from a cognitive and biological perspective, focusing on the brain and chemical imbalances. Thus, results of this study suggest that individuals in rural communities might lack a holistic understanding of mental health.


Our findings add to the literature by providing context for rural individuals’ perceptions and possible explanations for treatment and help-seeking patterns. Rural residents may be especially vulnerable to misinformation about mental health disorders because of mental illness stigma, a cultural expectancy of self-reliance to resolve mental health concerns, and ascertaining mental health–related information from nonprofessionals (e.g., family members, religious leaders; Smalley et al., 2012; Snell-Rood et al., 2017), thus further underscoring the importance of improving MHL in rural communities. In the current study, for example, many participants listed only one component of mental health (e.g., brain imbalance, thoughts), suggesting that their understanding of the concept of mental health is lacking. The focus on the biological composition of the brain in mental health is consistent with definitions of mental illness promoted by organizations such as the National Institute of Mental Health. Thus, although participants’ definitions of mental health are not incorrect, in many ways the focus is narrow and not comprehensive. Study participants excluded emotions and when speaking about biology focused on the brain, which potentially discounts somatic manifestations of mental illness (e.g., stomach pains).


A more comprehensive understanding of mental health, with a specific focus on the connection between emotions, behaviors, and somatic symptoms, could potentially assist rural residents with becoming more conscious of signs and symptoms related to common mental health concerns such as anxiety and depression (Kim et al., 2015). It seems important for mental health educators, organizations, and counseling practitioners in rural communities to provide education that broadens the beliefs about the nature of mental health. Educational campaigns and direct work that are more inclusive and broadly focused could be of benefit.


Implications for Professional Counselors

     Professional counselors and related mental health practitioners in rural areas noted they need training opportunities focused on clinical issues that are important in rural settings (Fifield & Oliver, 2016). Thus, the results from this study may offer mental health professionals guidance for reaching residents in rural communities and providing efficacious mental health services. Foremost, counselor training programs could consider developing courses with a specific focus on rural populations, which can assist counseling students in increasing their understanding of the culture of rural settings, how rural residents comprehend mental illness, and effective counseling practices in rural communities (Crumb, Mingo, & Crowe, 2019; Rollins, 2010). For example, counselors in training would be privy to facts such as how many people living in rural areas across the United States face additional life stressors, including poverty and housing and food insecurities, that impact their mental health and well-being.


The results of this study also illustrate the importance of building partnerships and collaborative relationships in rural communities, as rural residents may present varied concerns (e.g., concerns about physical health, family members, finances, spirituality) to counselors when seeking help. Thus, building both informal and formal professional support networks in rural communities is vital. Counselors in rural communities may consider building resources with physicians, faith-based organizations, and other mental health providers for consultation purposes (Avent, Cashwell, & Brown-Jeffy, 2015; Crumb, Mingo, & Crowe, 2019).


El-Amin et al. (2018) suggested programs such as rural-focused Mental Health First Aid to help increase MHL in rural communities. Because access to mental health services is often limited and/or non-existent in rural communities, counselors and related mental health professionals should be more intentional in implementing these forms of programming because of the large number of residents who reside in rural communities who have not yet been helped (El-Amin et al., 2018). Trainings such as this may assist with MHL, as well as mental health stigma, which has been associated with MHL in rural areas (Crowe et al., 2018).


Last, the focus on cognition in participants’ definitions of mental health may indicate a positive response to more cognitive-based theories such as CBT (A. T. Beck, 1970) and rational emotive behavioral therapy (Ellis, 1962). As this study was framed through the lens of CBT and the notion that cognition impacts emotions and behaviors, this theory and related interventions may fit well when working with clients in the rural United States. This type of “matching” related to how clients in the rural parts of the United States understand mental health (with a more cognitive focus) might lead to increased participation in counseling and therapy in rural areas. This might be a way for practitioners to join with the client initially, at the beginning of the therapeutic relationship, in order to “speak the same language.” However, given the findings of the current study, although individuals may have a natural inclination toward more cognitively focused theories, it is incumbent upon mental health professionals to challenge individuals to consider the ways emotions and behaviors are connected to their mental health as well. CBT fits this model well; however, scholars have also found ways to integrate other theories to provide an even more comprehensive and culturally responsive theoretical framework for clients. For example, one of the suggested interventions in Crumb and Haskins’ (2017) integration of CBT and relational cultural theory is to “apply cognitive restructuring through relational resilience” (p. 268). This technique could be especially beneficial to rural communities, as it honors the focus of cognitions while also considering how these thoughts and messages may be related to a broader systemic and environmental influence (Crumb & Haskins, 2017).


In sum, counselors should be aware that many of their clients may present with low MHL. Thus, education and awareness about mental health, diagnoses, and symptomatology may be an integral part of the treatment process. Counselors should consider this intentionality in education as a part of their role as advocates for their clients (Crumb, Haskins, & Brown, 2019).


Limitations and Future Directions

As with all research, the current study is not without limitations. First, the qualitative responses received from participants were often brief. The study team was able to analyze responses, but future qualitative research on the topic of MHL in rural samples might include a focus group design or individual interviews rather than paper-and-pencil surveys to get an in-depth look at how those in rural areas define mental health. Also, future research could seek to further understand the concept of well-being as used by rural participants, looking more in depth at all components (cognition, emotion, and behavior). Future research studies could also investigate the reasons for a focus on cognitive aspects of mental health. As it is impossible to separate cognition from emotion and behavior, this study found that many participants seemed to focus on cognition rather than a more comprehensive understanding of cognition as it related to choices in behaviors and affect. The current study took place in a medical center, and participants who were approached to participate may have felt pressure to complete the survey or answer in a way that was socially desirable. The sample was a convenience sample and may not be representative of others in the rural Southeast.


Large scale quantitative studies might offer scholars interested in MHL the opportunity to use validated instruments to measure literacy and perceptions about mental health in rural samples. Assessments such as the Mental Health Knowledge Schedule (Evans-Lacko et al., 2010) have been used in recent research (Crowe et al., 2018) to measure mental health knowledge. The Revised Fit, Stigma, & Value Scale (Kalkbrenner & Neukrug, 2018) is another scale measuring barriers to counseling such as stigma, values, and personal fit. These types of assessments can measure levels of recognition, familiarity, and attitudes toward mental health conditions in order to measure MHL and perceptions of mental health stigma.




This qualitative study investigated the HHS definition of mental health to determine if it was representative of rural Southeastern participants’ definitions. This assisted with answering the call for more research on the mental health of rural residents (Simmons, Yang, Wu, Bush, & Crofford, 2015) in order to provide better services to this population. Most participants demonstrated a conceptualization that included cognition, as well as well-being, and were more concrete in their conceptualization of mental health when compared to the more comprehensive HHS definition. A promising result from this study was that many participants seemed willing to seek mental health treatment in the future. Rural communities could benefit from mental health education with a holistic approach. Future research should consider interviewing rural populations to gather more detail on their definitions and understanding of mental health. The results provided interventions for professional counselors and related mental health clinicians, particularly those in rural settings, to integrate into their present work, pointed to the need for educational campaigns on mental health in rural areas, and highlighted areas for future research exploration.



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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Allison Crowe is an associate professor at East Carolina University. Paige Averett is a professor at East Carolina University. Janeé R. Avent Harris, NCC, is an assistant professor at East Carolina University. Loni Crumb, NCC, is an assistant professor at East Carolina University. Kerry Littlewood is an instructor at the University of South Florida. Correspondence can be addressed to Allison Crowe, 225 Ragsdale Hall, Mailstop 121, College of Education, 5th St., Greenville, NC 27858,