Jun 3, 2019 | Volume 9 - Issue 2
Patrick R. Mullen, Nancy Chae, Adrienne Backer
The Recognized American School Counselor Association Model Program (RAMP) designation aims to acknowledge school counselors who deliver comprehensive data-driven programs. However, there is little research to date that examines RAMP schools and associated factors with this designation. Therefore, we compared the characteristics of schools that earned the RAMP designation with a random sample of schools without this designation to examine if differences exist. Data was accessed using the Elementary/Secondary Information System through the U.S. Department of Education. The results indicated that non-RAMP schools in this study were more likely to: (a) be eligible for Title I; (b) be located in city, rural, and township communities; and (c) have fewer students and full-time equivalent employees. Furthermore, non-RAMP schools had higher rates of students eligible for free or reduced lunch. The development of support mechanisms for the RAMP-seeking process for these schools may be beneficial along with further research on this topic.
Keywords: Recognized ASCA Model Program (RAMP), school counseling, school characteristics, U.S. Department of Education, data-driven
School counselors provide an array of services to students and families across elementary and secondary schools. The American School Counselor Association (ASCA) created the ASCA National Model (ASCA, 2012), a framework for school counselors to identify the appropriate roles and duties of a school counselor. Additionally, the ASCA National Model outlines the tenets of comprehensive school counseling programs. Currently, the ASCA National Model is the only structured framework promoted by ASCA that recommends job duties and different roles that will help to support the school community (ASCA, 2012). For example, ASCA recommends that school counselors spend 80% or more of their time in providing direct or indirect service with the students in their buildings and 20% or less in program planning or school support (ASCA, 2012). Thus, this model is taught in school counselor training programs and used for professional development of practicing school counselors across the United States. One initiative by ASCA to encourage and recognize rigorously implemented school counseling programs is to facilitate the Recognized ASCA Model Program (RAMP) designation program (ASCA, 2019). RAMP is earned by school counseling programs that consistently adhere to the ASCA National Model and demonstrate its implementation and outcomes through data-driven practices. Programs with the RAMP designation are highlighted at ASCA-related events and publications. The RAMP initiative has encouraged many school counseling programs to implement comprehensive services and requires evaluation of their effectiveness through data-driven practices.
While the RAMP recognition intends to highlight accomplished school counseling programs, the general development of the ASCA National Model helped to structure the efforts and experiences of school counselors and students. Researchers have previously asserted that the ASCA National Model can benefit student achievement and promote effective school counseling programs (Brigman & Campbell, 2003; Carey, Harrity, & Dimmitt, 2005; Sink & Stroh, 2003). In a study of secondary school counselors from Michigan, Pyne (2011) suggested that school counselors who implemented a comprehensive school counseling program, like the ASCA National Model, experienced greater job satisfaction compared to school counselors without such programs. Specifically, school counselors exhibited greater job satisfaction when school counseling programs had administrative support, included communication among school faculty members, possessed a clear program philosophy, identified clear roles of the school counselor, served all students in the school, and included time for planning and evaluation of the school counseling program and related activities (Pyne, 2011).
In studies of state-based school counseling programs, researchers have found positive features of student outcomes in schools with comprehensive school counseling programs. Carey, Harrington, Martin, and Hoffman (2012) assessed school counseling programs in suburban and rural Nebraska high schools, and found that well-implemented and differentiated programs with features of the ASCA National Model enhanced student outcomes, including lower suspension rates, lower discipline incident rates, higher attendance rates, and higher math proficiency. By contrast, high school counselors in Nebraska who spent more time providing responsive services were associated with schools with higher suspension and disciplinary incident rates and lower graduation rates. Moreover, Carey, Harrington, Martin, and Stevenson (2012) assessed school counseling programs in Utah high schools, and found that high schools that reflected components of the ASCA National Model improved student achievement, such as ACT scores, number of students taking the ACT, and percentage of students with proficient reading and math scores on the state assessments. The researchers suggested that programmatic focus and use of data were strongly associated with academic achievement and college aspirations in Utah high schools (Carey, Harrington, Martin, & Stevenson, 2012). Carey, Harrington, Martin, and Stevenson (2012) also found that more favorable or lower student-to-school counselor ratios were connected to decreased disciplinary issues and increased student attendance.
Lapan, Gysbers, and Petroski (2001) found that students who attended Missouri middle schools with fully implemented comprehensive school counseling programs reported feeling safer and having fewer conflicts with peers, having improved relationships with teachers, and believing their education was applicable to their future, as compared to students who attended schools with lower implementation fidelity. Additionally, Sink, Akos, Turnbull, and Mvududu (2008) compared student achievement in middle schools in Washington with and without fully implemented comprehensive school counseling programs and found student achievement was significantly higher in schools with fully implemented comprehensive school counseling programs for at least five years. Both studies indicated positive student outcomes associated with the implementation of comprehensive school counseling programs. However, despite a call for schools and school counselors to implement comprehensive school counseling programs for more than 30 years, Martin, Carey, and DeCoster (2009) found that 17 states have fully implemented these programs and 24 states have at least partially implemented these programs.
Although previous research addressed how components of the ASCA National Model offer benefits to school counseling programs and schools, there is little known about how schools that earn a RAMP designation uniquely aid students’ academic, social and emotional, and postsecondary outcomes. In other words, there is limited research about the differences between schools with a RAMP designation versus schools without a RAMP designation (henceforward non-RAMP). In one study, Wilkerson, Pérusse, and Hughes (2013) compared RAMP and non-RAMP designated schools on their Adequate Yearly Progress scores for Math and English/Language Arts and found that the elementary schools with RAMP performed better than non-RAMP schools. However, the researchers only collected data from a single state, had a limited sample size resulting in issues related to power, and did not control for school factors (e.g., funding, size, and student characteristics) that may have impacted the results. Outside of this single study, no other research has been done that provides empirical evidence for RAMP designated schools being more effective at addressing students’ educational outcomes over non-RAMP schools.
Other studies about RAMP schools connected the benefits of data-driven decision making, supervisory practices, and administrative support. In a study of school counselors from RAMP schools, Young and Kaffenberger (2011) found that participants who earned RAMP actively used data to drive and inform school counseling program development and impact student outcomes. In addition, school counselors reported that undergoing the RAMP application process transformed their beliefs in using data to address gaps and develop interventions (Young & Kaffenberger, 2011). In addition, Blakely, Underwood, and Rehfuss (2009) found that supervisors in a RAMP school provided significantly more supervisory activities related to the ASCA National Model for school counseling trainees in RAMP schools than trainees in traditional schools (i.e., non-RAMP schools), which may help to maintain consistency in school counseling training and support trainees to apply their university training in their professional practice. Moreover, in a study of administrators’ perceptions of school counselors in RAMP versus non-RAMP schools, Dodson (2009) found that participants from RAMP schools more often perceived school counselors to deliver classroom guidance lessons, counsel students with disciplinary concerns, consult with teachers, and interpret student records, compared to participants from non-RAMP schools. According to these studies, there are benefits of understanding the RAMP process in schools to inform training practices and elicit administrative support.
One topic related to becoming a RAMP-designated school is the ability of a counseling program to implement the components of the ASCA National Model with fidelity. To implement a comprehensive school counseling program, school counselors need the financial and time resources to implement the services. For example, the school or school counselor may need to put forth money to purchase various curricula for group or classroom interventions. Moreover, ASCA suggests that the recommended timeline of the RAMP process includes one to two years of planning (e.g., developing the foundational and management components, such as calendars, an advisory council, and advisory agreement) and approximately one year to collect and evaluate data (ASCA, 2019). A minimum 2-year commitment can be burdensome for school counseling programs with a single school counselor and even for a team of school counselors, which may require coordination. In addition, school counselors often have high student caseloads and do not always have the time to implement the various components of the ASCA National Model because they focus on responding to immediate student needs and non–counselor-related duties (McCarthy, van Horn Kerne, Calfa, Lambert, & Guzmán, 2010). Increased financial resources and counselors in a school (i.e., lower student-to-counselor ratio) impact the ability of school counselors to implement the ASCA National Model (Lapan, Whitcomb, & Aleman, 2012). As a result, schools with fewer staff allocations and fewer financial supports may be less likely to put forth time and resources to the RAMP designation.
In addition, the application for RAMP costs $250 for ASCA members and $500 for non-members, which adds to the financial burden of schools to pay to implement these services. There also is a perceived lack of benefit for earning RAMP designation. School districts and states have yet to incentivize the RAMP designation, making the use of time and financial effort toward this status resultant in only professional recognition (ASCA, 2019). Given the emphasis placed on the ASCA National Model and the RAMP designation, those schools with the fewest resources may likely have the least amount of opportunity to implement. However, there has been no research on the differences in school characteristics for those sites that have earned the RAMP designation in comparison to those schools who have not earned this recognition. Therefore, the purpose of this study was to compare the characteristics of RAMP-designated schools to a sample of non-RAMP schools to provide information about how these schools differ.
While earning the RAMP designation is an indicator of the comprehensive implementation of the ASCA National Model, little is known about characteristics of schools that have attained RAMP recognition in comparison to non-RAMP schools. The lack of research on RAMP schools is notable due to ASCA’s efforts to train and encourage practitioners to earn this recognition, which may take school counselors away from other responsibilities or burden them with more commitments. It is likely that school counseling programs that pursue RAMP have unique qualities as compared to non-RAMP schools, given the requirements of RAMP, which necessitate resources and organizational support. Some differences between RAMP and non-RAMP schools might lie in the school counselors’ individual qualities (e.g., professional identity, training, motivation); however, there could be characteristics of the school that differ (e.g., school size or location) and facilitate or hinder the achievement of RAMP designation. Therefore, we compared differences in school characteristics based on whether a school has achieved RAMP status. The following exploratory research questions guided our study: (1) Do schools whose school counseling programs have achieved RAMP differ in general school characteristics when compared to schools with school counseling programs that have not achieved RAMP status? (2) Do schools whose school counseling programs have achieved RAMP differ in student body characteristics when compared to schools with school counseling programs that have not achieved RAMP status?
Method
Data Sources
The analyses in this study utilized school-level data publicly available from the Common Core of Data’s (CCD) Elementary/Secondary Information System (ELSi; National Center for Education Statistics, 2018) to retrieve the school characteristics for a sample of RAMP schools and non-RAMP schools. The CCD is a census database that provides information on all public elementary and secondary schools along with school districts and additional administrative and operational entities in the United States. Education agencies submit data to the National Center for Education Statistics on an annual basis (National Center for Education Statistics, 2018). In the data set, three types of information are collected: (a) general descriptive data (e.g., school grade level and locale), (b) demographic data on staff and students, and (c) fiscal data.
We accessed the ELSi to retrieve information on general descriptive data and demographic data. In our first step, we downloaded a dataset of every U.S. public school from the most recent year available (2015–2016) that contained characteristics for each school. We captured information about free and reduced lunch rates (i.e., based on family size and income criteria, students eligible for free or reduced-price lunches at school under the National School Lunch Act), Title I status (i.e., per state and federal regulations, Title I schools are eligible for participation in programs authorized by Title I of Public Law 103-382), geographic region in which the school is located, grade level, number of students at the school, race and ethnicity demographics for each school, and school full-time–equivalent (FTE) teachers. Then, we removed schools (n = 133) that attained RAMP status in 2015 or 2016 and created a new dataset with these schools. We selected the RAMP schools from the 2015–2016 school year to match the years in which the CCD was represented. The list of RAMP schools was acquired through the ASCA website. After removing RAMP schools, we generated an equal-sized simple random sample of schools (n = 133) from the remaining schools in the CCD database. The resulting aggregated and de-identified sample included data for 266 schools across the United States. There were some cases in which data was missing (e.g., three schools didn’t report grade level served).
Participants
The sample (N = 266) in this study included RAMP (n = 133, 50%) and non-RAMP (n = 133, 50%) schools from across the United States. On average, the schools in this sample reported 940.96 (SD = 753.76, Mdn = 706.00, Range = 35 to 4,190) students, a mean teacher-to-pupil ratio of 16.80 (SD = 4.72, Mdn = 16.18, Range = 8.57 to 53.56), and a mean FTE of 55.43 (SD = 42.69, Mdn = 43.60, Range = 0 to 270.96). In addition, the average percentage of students eligible for free or reduced lunch was 48.33% (SD = 26.81, Mdn = 46.30, Range = 2.32 to 100), and the majority of schools were eligible for Title I funding (n = 159, 59.8%) as compared to not being eligible for Title I funding (n = 107, 40.2%). We used percentages of the student body that make up each race and ethnicity group by dividing the number of students for each group by the total number of students in the school and multiplying it by 100. Across all the schools that reported the race and ethnicity rates in this study (N = 261), White students had the highest mean percentage (M = 52.30%, Mdn = 55.38%, SD = 29.26%) followed by Hispanic (M = 19.94%, Mdn = 12.44%, SD = 21.82%), Black (M = 17.47%, Mdn = 8.28%, SD = 22.20%), Asian (M = 4.93%, Mdn = 2.04%, SD = 7.54%), Two or more races/ethnicities (M = 3.99%, Mdn = 3.33%, SD = 3.13%), Hawaiian or Pacific Islander (M = .74%, Mdn = .05%, SD = 5.81%), and American Indian (M =.69%, Mdn = .22%, SD = 2.78%).
Regarding location, the ELSi portal identifies locales, which measure schools’ locations relative to the populated areas in which they are situated, as city, suburban, town, and rural settings. There are 12 subdomains to indicate varied levels within the broad domains: City: Large, Midsize, and Small; Suburb: Large, Midsize, and Small; Town: Fringe, Distant, and Remote; and Rural: Fringe, Distant, and Remote (National Center for Education Statistics, 2018). For this study, we condensed these subcategories into four broad areas to simplify the analyses. Most schools were located in suburban communities (n = 120, 45.1%) followed by city (n = 71, 26.7%), rural (n = 53, 19.9%), and town (n = 22, 8.3%). The majority of the schools were primary level (n = 111, 41.7%) followed by secondary level (n = 79, 29.7%), middle (n = 65, 24.4%), and other levels (n = 8, 3.0%), with three (1.1%) cases of missing data.
ELSi denotes two school-choice programs: (a) charter schools—schools that offer elementary and secondary education for students who are eligible under a charter approved by the state legislature or some other applicable authority and (b) magnet schools—schools that offer programs to draw students of varied racial and ethnic backgrounds with the aim to decrease racial isolation and offer an academic and social focus. Two-hundred and forty-three (91.4%) of the schools were not charter schools, 11 (4.1%) schools identified as charter schools, and 12 schools did not have data for this category. Only 29 (10.9%) schools in the sample identified as magnet schools, 222 (83.5%) schools were not magnet schools, and 15 (5.6%) schools had missing data.
Study Variables
The two-level independent variable in this study was whether a school achieved RAMP status. The dependent variables included general descriptive data and demographic data on students. The general descriptive dependent variables of school characteristics (Research Question 1) included grade level served by the school (i.e., elementary, middle, high school), geographic location of the school (i.e., city, suburban, town, and rural), FTE, and total number of attending students. Furthermore, the student demographic data dependent variables (Research Question 2) included percentage of students eligible for free or reduced lunch, Title I status of the school, and percentage of race and ethnicity in the student body. For percentage of students eligible for free or reduced lunch and percentage of race/ethnicity in the student body, we calculated these variables using the frequency count data. All dependent variables were selected by using the filter option in ELSi.
Data Analysis
We employed the Mann-Whitney U Test and chi-square analyses for this study due to the data characteristics. Specifically, each analysis included RAMP status as a nominal and dichotomous independent variable. The dependent variables were nominal with four groups or continuous data. However, the distribution of the continuous dependent variables violated assumptions for normality; thus, we applied non-parametric approaches of data analysis to this data. The Mann-Whitney U Test was used with continuous dependent variables. For the Mann-Whitney U Tests, we interpreted the effect sizes by computing the approximate value of r (Pallant, 2011), which could be interpreted using 0.1, 0.3, and 0.5 for small, medium, and large effect sizes, respectively (Cohen, 1988). We also utilized chi-square tests for independence when the dependent variables were nominal. In the case of a two-by-two chi-square table, we used Yates’ continuity correction statistics for interpretation and the phi coefficient to evaluate the effect size. The phi coefficient can be interpreted in a similar fashion as the r statistic. For analyses with chi-square tables of two-by-four, we studied the Pearson chi-square statistic and the Cramer’s V effect size statistic. We interpreted the Cramer’s V based on criteria for four categories (0.06, 0.17, and 0.29 were small, medium, and large effect sizes, respectively; Pallant, 2011). An initial a priori power analysis for the Mann-Whitney U Test using G*Power with an alpha level of .05, power established at .95, and a moderate effect size of 0.5 (Cohen, 1988) identified a minimum sample size of 184. Similarly, we conducted an a priori power analysis for the chi-square tests for independence using G*Power with an alpha level of .05, power established at .95, and a moderate effect size of 0.3 (Cohen, 1988) and identified a minimum sample of 191. We used a Bonferroni corrected value of .003 as a means to reduce the likelihood of Type I errors.
Results
General School Characteristics
Our first research question examined whether schools whose school counseling programs have achieved RAMP (i.e., RAMP schools) differ in general school characteristics when compared to schools with school counseling programs that have not achieved RAMP status (i.e., non-RAMP schools). We facilitated a Mann-Whitney U Test to compare the total number of students per school for both RAMP and non-RAMP schools. The Mann-Whitney U Test revealed a statistically significant difference in RAMP schools (Mrank = 159.90, Mdn = 925, M = 1,201.81, SD = 853.67) versus non-RAMP schools (Mrank = 103.96, Mdn = 575, M = 687.96, SD = 534.56, U = 4,915.50, z = -5.97, p < .001, r = .37). Similarly, we completed the Mann-Whitney U Test to analyze FTEs for both RAMP and non-RAMP schools. The Mann-Whitney U Test revealed a statistically significant difference in FTE for schools that had RAMP (Mrank = 159.20, Mdn = 51.37, M = 69.38, SD = 48.49) and those schools that did not have RAMP (Mrank = 105.80, Mdn = 32.48, M = 41.49, SD = 30.27, U = 5,187.00, z = -5.68, p < .001, r = .35).
A chi-square test for independence indicated a statistically significant association between RAMP and geographic location among the schools in this study: χ2 (3, N = 266) = 22.94, p < .001, Cramer’s V = .29. Table 1 provides a breakdown of the frequency and percentage for each geographical location by RAMP status. Non-RAMP schools were more often located in city, town, and rural settings than RAMP schools, whereas RAMP schools were more often located in suburban locations. A chi-square test for independence indicated no statistically significant association between RAMP and school level among the schools in this study: χ2 (3, N = 263) = 22.94, p = .06, Cramer’s V = .17 (Bonferroni corrected p value of .003).
| Table 1
Chi-square Tests of Independence Comparing RAMP Versus Non-RAMP Schools |
| Independent Variable |
RAMP
(n = 133) |
Non-RAMP (n = 133) |
Pearson χ2 |
Cramer’s V |
| Geographic Location |
|
|
22.94** |
.29 |
| City (n = 71) |
28 (39.4%) |
43 (60.6%) |
|
|
| Suburban (n = 120) |
79 (65.8%) |
41 (34.3%) |
|
|
| Town (n = 22) |
6 (27.3%) |
16 (72.7%) |
|
|
| Rural (n = 53) |
30 (37.7%) |
33 (62.3%) |
|
|
| School Level |
|
|
7.61 |
.17 |
| Primary (n = 111) |
45 (40.5%) |
66 (59.5%) |
|
|
| Middle (n = 65) |
33 (50.8%) |
32 (49.2%) |
|
|
| Secondary (n = 79) |
48 (60.8%) |
31 (39.2%) |
|
|
| Other (n = 8) |
4 (50.0%) |
4 (50.0%) |
|
|
|
|
|
Cont. Correlation |
Phi
|
| Title I Eligible |
|
|
33.08** |
-.36 |
| Yes (n = 159) |
56 (35.2%) |
103 (64.8%) |
|
|
| No (n = 107) |
77 (71.0%) |
30 (28.0%) |
|
|
| Charter School |
|
|
5.33* |
-.16 |
| Yes (n = 11) |
1 (9.1%) |
10 (90.9%) |
|
|
| No (n = 243) |
120 (49.4%) |
123 (50.6%) |
|
|
| Magnet School |
|
|
6.17* |
.17 |
| Yes (n = 29) |
21 (72.4%) |
8 (27.6%) |
|
|
| No (n = 222) |
102 (45.9%) |
120 (54.1%) |
|
|
| Note. * = p < .05, ** = p < .001, Bonferroni correction of .003 for significant p value. |
A chi-square test for independence using Yates’ continuity correction indicated a non-statistically significant association between RAMP status and identity as a charter school among the schools in this study: χ2 (1, N = 254) = 5.33, p < .05, phi = -.16 (Bonferroni corrected p value of .003). Of the 11 schools that were charter schools, 10 (90.9%) were non-RAMP schools and one (9.1%) was a RAMP school. However, schools that were not charter schools were evenly split between RAMP schools (n = 120, 49.4%) and non-RAMP schools (n = 123, 50.6%). Similarly, another chi-square test for independence using Yates’ continuity correction indicated no statistically significant association between RAMP status and identification as a magnet school among the schools in this study: χ2 (1, N = 251) = 6.17, p < .05, phi = .17 (Bonferroni corrected p value of .003). Nonetheless, schools that identified as magnet schools (N = 29) were more often RAMP schools (n = 21, 72.4%) compared to non-RAMP schools (n = 8, 27.6%). Of the schools that did not identify as a magnet school (n = 222), 45.9% (n = 102) were RAMP and 54.1% (n = 120) were not RAMP.
Student Body Characteristics
The second research question examined whether schools whose school counseling programs have achieved RAMP differ in student body characteristics when compared to schools with school counseling programs that have not achieved RAMP status. A chi-square test for independence using Yates’ continuity correction indicated a significant association between RAMP status and Title I eligibility among the schools in this study: χ2 (1, N = 266) = 33.08, p < .001, phi = -.36. Of the schools eligible for Title I (n = 159), 56 (35.2%) were RAMP schools and 103 (64.8%) were non-RAMP schools. Conversely, 77 (71.0%) of the schools not eligible for Title I (n = 107) were RAMP schools, whereas 30 (28.0%) were non-RAMP schools. A Mann-Whitney U Test revealed a significant difference in the percentage of students eligible for free and reduced lunch based on RAMP (Mrank = 114.19, Mdn = 38.71, M = 42.23, SD = 26.16) and those schools that did not have RAMP (Mrank = 148.29, Mdn = 53.63, M = 54.24, SD = 26.18, U = 6,345.00, z = -3.64, p < .001, r = .23).
Table 2 provides a detailed breakdown of the percentages of students’ race and ethnicity for RAMP and non-RAMP schools. The percentages were calculated by dividing the total number of students identified for each race/ethnic category by the total number of students at each school. Percentages were utilized versus total frequency counts to help understand the rates of students for each race and ethnicity category in the contexts of their schools. Of the race and ethnicity categories, one produced significant differences based on RAMP status. The RAMP schools in this study had a greater percentage of Asian students when compared to non-RAMP schools.
| Table 2
Breakdown of Percentages of Students’ Race/Ethnicity for RAMP and Non-RAMP Schools |
|
Percentages for Each Race/Ethnicity Classification by RAMP Status |
|
|
|
|
RAMP |
Non-RAMP |
|
|
|
| Race/Ethnicity |
Mrank |
M |
SD |
Mrank |
M |
SD |
U |
z |
r |
| White |
128.90 |
57.96 |
26.92 |
133.50 |
52.64 |
31.47 |
8,243.00 |
-0.44 |
– |
| Black |
141.12 |
16.94 |
19.24 |
121.11 |
17.98 |
24.81 |
7,209.00 |
-2.14 |
– |
| Hispanic |
133.15 |
18.58 |
18.49 |
128.90 |
21.27 |
24.64 |
8,237.00 |
-0.45 |
– |
| Asian |
152.80 |
6.38 |
8.47 |
109.69 |
3.51 |
6.23 |
5,701.50 |
-4.62* |
.29 |
| Hawaiian Pacific Islander |
137.85 |
1.24 |
8.23 |
124.30 |
0.24 |
0.60 |
7,630.00 |
-1.54 |
– |
| American Indian |
126.31 |
0.50 |
1.74 |
135.59 |
0.88 |
3.51 |
7,908.50 |
-1.00 |
– |
| Two or more races |
146.31 |
4.33 |
3.12 |
119.79 |
3.56 |
3.13 |
7,021.50 |
-2.81* |
– |
| Note. * = p < .001 |
Discussion
The first research question compared school characteristics of RAMP and non-RAMP schools, and we found that RAMP schools were more likely to have a larger student enrollment and more full-time teachers compared to non-RAMP schools. In addition, RAMP schools were more likely to be located in suburban areas, whereas non-RAMP schools were more often in city, town, and rural settings. RAMP schools were more likely to be magnet schools and less likely to be charter schools; however, this was not found to be significant with the Bonferroni corrected p value. There were no differences in school level (i.e., elementary, middle, high) and pupil-to-teacher ratios as variables in either RAMP or non-RAMP schools. The second research question compared student body characteristics of RAMP and non-RAMP schools, and we found that non-RAMP schools were more likely to be Title I schools and serve low-income students compared to RAMP schools. Moreover, RAMP schools likely had more Asian students. There is little known about RAMP schools in relationship to students’ demographic breakdown, and this finding provides some insight into the topic for continued research. This finding has a medium effect size, which indicates moderate practical significance. More research on the racial/ethnic breakdown of RAMP compared to non-RAMP schools is needed to make significant claims about this difference.
Although RAMP schools tended to have larger student enrollments than non-RAMP schools, RAMP schools were also likely to have more full-time teachers. With larger student bodies, more full-time staff might be needed and budgeted to address the capacity of students served. However, the data showed that larger school enrollments were often located in suburban areas. This finding raises the question about how certain contextual factors of schools play a role in comprehensive school counseling program development. For instance, it is possible that largely populated urban, township, or rural schools may have fewer full-time teachers, making it difficult to implement comprehensive counseling programs (Gagnon & Mattingly, 2016). With more full-time staff, school counselors who are pursuing the RAMP application process may benefit from increased access to full- and part-time staff to support program development; however, a report by Scafidi (2013) found that an increase in staffing in U.S. public schools did not necessarily appear to have positive outcomes for student achievement, such as test scores and graduation rates. More research is needed to understand how numbers of school staff members can support school counselors and counseling program development, implementation, and recognition. More importantly, students and their families can benefit from having increased access to full-time personnel to address their academic, social and emotional, and postsecondary needs. For example, Sink (2008) suggested that when elementary school teachers work collaboratively with school counselors, student learning and academic outcomes have the potential to improve and narrow achievement gaps among students. On the other hand, fewer full-time staff might be budgeted in schools with lower enrollments, thus having to share and delegate the many daily roles and responsibilities among fewer staff. Furthermore, having fewer FTE teachers may increase staff members’ burdens, and the RAMP process could be perceived as additional tasks that take time away from their primary responsibilities.
Our results indicated the allocation of the RAMP designation differed based on location. The greater likelihood of RAMP schools being in suburban locations suggested that RAMP schools are often located in areas of increased access to school-based and community resources (Wright, 2012). With greater access to physical and financial resources, counselors can bridge and enhance their program planning and delivery for students. Since non-RAMP schools in this study were likely to be located in rural, township, and urban areas as well as serve more low-income students, these student populations might have less access to counseling services due to the challenges of funding and resource availability in their local communities. Also, these communities might serve higher populations of minority and low-income students (Gagnon & Mattingly, 2016; Lapan, Gysbers, & Sun, 1997; Lee, 2005; Sutton & Pearson, 2002).
Although magnet and charter schools offer attractive nontraditional school and program choices to students and families, Archbald (1996) suggested that magnet schools either appealed to parents of higher educational attainment, or parents of higher educational attainment were better able to gain access to magnet schools. Parents of higher educational attainment are likely to have greater financial resources, and in addition, because of specialized programming, some magnet schools have even received increased educational funding (Archbald, 1996). It is possible that families of higher educational attainment and greater funding can afford schools and their school counseling programs with more resources to implement comprehensive counseling programs. Moreover, in a case study of a college counseling program in a charter high school, researchers suggested that the innovative nature of the charter school framework and structure may support the work of college counseling; however, school counselors may experience difficulties in implementing a comprehensive college counseling model due to the organizational challenges of sustaining a new school (Farmer-Hinton & McCullough, 2008). Furthermore, charter schools may likely have smaller student enrollments and thus fewer full-time teachers budgeted for the programs, which connects to the present study’s findings about non-RAMP schools. Both magnet and charter programs attract students based on various program characteristics, and further studies about school counselors’ roles in school-choice programs is warranted. The ways in which schools are funded and managed can impact school counselors’ access to developing and implementing comprehensive school counseling programs. Further research is needed to explore the characteristics of these school-choice programs and their connections with comprehensive school counseling programs.
Teacher-to-student ratios were not different when comparing RAMP and non-RAMP schools in our study, which is consistent with the mixed evidence about the impact of teacher-to-student ratios on student achievement. For instance, one study found that lower teacher-to-student ratios did not necessarily equate to higher test achievement (Alspaugh, 1994), while another study showed that lower teacher-to-student ratios increased student achievement (Schwartz, Schmidt, & Lose, 2012). Further research is not only needed about the potential impact of teacher-to-student ratios on school counseling programming, but also student-to-school counselor ratios on program development and delivery. Researchers found that Connecticut, Missouri, Nebraska, and Utah high schools with comprehensive school counseling programs and lower student-to-school counselor ratios were connected to lower disciplinary rates and higher attendance rates (Carey, Harrington, Martin, & Hoffman, 2012; Carey, Harrington, Martin, and Stevenson, 2012; Lapan, Gysbers, Stanley, & Pierce, 2012; Lapan, Whitcomb, & Aleman, 2012). It also could be beneficial to further understand how student-to-school counselor ratios impact RAMP programming.
School counselors and the programs they develop play critical roles in closing the achievement gap (Holcomb-McCoy, 2007). RAMP schools submit closing-the-gap results reports as a component of the RAMP application to address an achievement or attainment gap within the context of their school and community, demonstrating that comprehensive school counseling programs work toward closing such gaps. It is possible that RAMP schools work toward closing the achievement and attainment gaps specific to their local settings; however, the findings of this study demonstrate that RAMP schools in totality might not be addressing the national educational gaps among students from low-income backgrounds. This study demonstrated that fewer low-income students and students who attended Title I schools are in RAMP schools, which highlights the issue of equity and access to comprehensive school counseling programs to support the academic, social and emotional, and postsecondary development of students. Dimmitt and Wilkerson (2012) found that schools in Rhode Island with higher percentages of minority students and those receiving free and reduced lunch were less likely to have implemented comprehensive school counseling programs, which supports the findings of the present study. In addition, researchers found that students who attended poorer, diverse, and city school districts had less access to school counselors (Gagnon & Mattingly, 2016). However, research has demonstrated that when schools reduce the student-to-school counselor ratio to 250:1, as recommended by ASCA, students receiving free and reduced lunch at high-poverty schools had better academic outcomes (Lapan, Gysbers, Stanley, & Pierce, 2012). Research should continue to explore and question how RAMP schools work toward more globally closing the achievement gap in addition to addressing the gaps within their own local contexts.
Implications for Practice and Research
The findings of this study indicate potential inequalities between RAMP-designated schools and non-RAMP schools. Specifically, the RAMP designation appears to be more often received in schools that: (a) have fewer students on free and reduced lunch, (b) have more students and FTEs, and (c) are less likely to be eligible for Title I. Thus, there are several implications for practice and research. School counselors whose principals are supportive and knowledgeable about school counselors’ roles and programming can better facilitate implementation of comprehensive school counseling programs (Dodson, 2009; Fye, Miller, & Rainey, 2018). When school counselors are burdened by non-counseling duties, such as administrative tasks, substitute teaching, and lunch duty, they are less likely to devote the time, energy, and resources required to effectively implement components of the ASCA National Model. Therefore, it is critical that school counselors and principals view the ASCA National Model not as an added task, but rather an inherent element that guides program development, enhances student achievement, and supports underrepresented student groups who would not otherwise have access. School counselors can work with school administrations to advocate for the time and financial resources needed to implement components of the ASCA National Model.
As a tool to advocate for the merit of the ASCA National Model and the RAMP designation, scholars can develop and implement research studies that test and evaluate the effectiveness of this approach. For instance, Martin and Carey (2014) developed a logic model to guide evaluation of ASCA National Model programs, which offered a step toward understanding the connection between comprehensive school counseling programs and addressing issues related to the student achievement gap and outcomes. Also, Villares and Dimmit (2017) identified the top research priorities in the school counseling field, indicating that determining best practices related to school counseling interventions persists as highly ranked, as does evaluating the impact of comprehensive school counseling programs on students’ academic development and achievement. Additional studies to test the effectiveness of the ASCA National Model are needed to attest to its merit as an evidence-based practice. For example, many evidence-based registries require interventions to have been researched using experimental or quasi-experimental designs, used an inactive control group, and been published in high quality journals (Brigman, Villares, & Webb, 2018; Mullen, Stevens, & Chae, 2019). Thus, researchers may want to develop rigorous study designs that provide merit for the ASCA National Model’s effectiveness—an endeavor that has yet to be fulfilled in the literature despite the vast implementation of this model. Similarly, ASCA as an organization would likely benefit from providing resources and support to researchers to take on such endeavors. The need for increased use of the ASCA National Model is predicated on its effectiveness at enhancing students’ educational, social and emotional, and career outcomes; consequently, research is vital to establish its credibility. Research on the effectiveness of the ASCA National Model will help develop its merit for stakeholders and enhance the ability to advocate for its implementation.
A key finding of our study is that schools that are lower staffed, smaller, and have students with lower SES are less likely to receive the RAMP designation. Based on the concept that higher implementation of the ASCA National Model will result in better student outcomes, it is imperative to increase access for schools with lower resources and higher needs. As the ASCA National Model asserts and ASCA as an organization believes school counselors to be agents of social justice, it is reasonable that measures are taken to increase the access to service implementation for smaller, lower staffed schools with a higher rate of students with lower SES. For example, ASCA could provide training materials or programs at a reduced rate for qualified schools or waive the application fee for schools that may not have access to such support locally. Similarly, ASCA could provide or facilitate mentor support for schools that may not have access to this type of support locally. Moreover, ASCA can support school counselors, especially those in Title I schools who serve larger populations of students and families who are from low SES backgrounds, by offering supervision or mentoring at no or limited cost to facilitate strengths-based partnerships with schools, families, and communities that have the potential to provide necessary resources and supports for students’ academic, social and emotional, and postsecondary development (Bryan & Henry, 2008). School counselors, school counseling trainees, and school counselor educators are encouraged to be self-reflective as well as to engage in professional development practices connected to supporting students and families from low SES backgrounds (Cole & Grothaus, 2014). School counselors can gain awareness of and advocate for the challenges experienced by these students and families and also highlight their strengths and assets. While it is unlikely that any one individual or organization can cause a school to increase the number of school counselors at that site, it is relevant to continue advocacy efforts related to decreasing student ratios.
Limitations and Future Research Directions
This study compares school and student characteristics of RAMP and non-RAMP schools; however, the results do not attribute causality. Based on the findings, we can only make predictions based on the given characteristics of RAMP and non-RAMP schools. Another limitation is that CCD ELSi data neither identifies if schools have a presence of school counselors nor clarifies if schools include school counselors in the FTE category. We can be assured that the RAMP schools in this study have at least one school counselor, but it is unclear if school counselors are represented in our simple random sample of non-RAMP schools. Moreover, since there were only 133 RAMP schools in the 2015–2016 school year, the 133 non-RAMP schools selected for this study might not necessarily be an accurate representation of all U.S. public schools. Also, this study cannot account for or consider the individual qualities of school counselors in RAMP schools and how individual school counselors’ professional identity, training, motivation, and other unique factors contribute to RAMP achievement.
Future research can explore the barriers and supports of pursuing and sustaining RAMP, like in Fye et al. (2018). Continued research is needed to understand how RAMP schools specifically address and work toward closing the achievement gap, which impacts students of color and students from low-income backgrounds. Furthermore, although there are existing state-level studies of school counseling programs and their connections to student outcomes within individual states (Burkard, Gillen, Martinez, & Skytte, 2012; Carey, Harrington, Martin, & Hoffman, 2012; Carey, Harrington, Martin, & Stevenson, 2012; Dimmitt & Wilkerson, 2012; Lapan, Gysbers, Stanley, & Pierce, 2012; Lapan, Whitcomb, & Aleman, 2012; Martin et al., 2009; Sink et al., 2008; Wilkerson et al., 2013), cross-comparison studies of state-by-state programs can be useful to see which states are highly represented among RAMP schools, and how these states’ RAMP schools effectively facilitate the RAMP process. Such state-based studies also can explore the extent to which state-level funding and supports impact school counseling program development.
Conclusion
This study explored whether schools whose school counseling programs have achieved RAMP designation differ in general school and student body characteristics when compared to schools with school counseling programs that have not achieved RAMP status. The study utilized publicly available data from the CCD’s ELSi to retrieve the school characteristics for RAMP schools and an equal-sized simple random sample of non-RAMP schools. The results showed that general school characteristics of RAMP schools differed from non-RAMP schools. Non-RAMP schools tended to be eligible for Title I, had more students eligible for free and reduced lunch, and were more likely to be in city, rural, and township communities. Non-RAMP schools also had fewer students and full-time teachers compared to RAMP schools. This study not only addressed issues of social justice as it pertains to socioeconomic status, geographic location, and race, but also explored the disparities in the types of schools and student populations that have or lack access to school counseling programs. School counselors, schools, and ASCA can collaborate and advocate on behalf of students to ensure that comprehensive school counseling programs serve and are equitably accessed by all students.
Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.
References
Alspaugh, J. W. (1994). The relationship between school size, student teacher ratio and school efficiency. Education, 114, 593–602.
American School Counselor Association. (2012). The ASCA National Model: A framework for school counseling programs (3rd ed.). Alexandria, VA: Author.
American School Counselor Association. (2019). Recognized ASCA Model Program (RAMP). Retrieved from https://www.schoolcounselor.org/school-counselors/recognized-asca-model-program-(ramp)
Archbald, D. (1996). SES and demographic predictors of magnet school enrollment. Journal of Research and Development in Education, 29, 152–162.
Blakely, C., Underwood, L. A., & Rehfuss, M. (2009). Effectiveness of school counselor supervision with trainees utilizing the ASCA Model. Journal of School Counseling, 7.
Brigman, G., & Campbell, C. (2003). Helping students improve academic achievement and school success behavior. Professional School Counseling, 7, 91–98.
Brigman, G., Villares, E., & Webb, L. (2018). Evidence-based school counseling: A student success approach. New York, NY: Taylor & Francis.
Bryan, J., & Henry, L. (2008). Strengths-based partnerships: A school-family-community partnership approach to empowering students. Professional School Counseling, 12, 149–156. doi:10.1177/2156759X0801200202
Burkard, A. W., Gillen, M., Martinez, M. J., & Skytte, S. L. (2012). Implementation challenges and training needs for comprehensive school counseling programs in Wisconsin high schools. Professional School Counseling, 16, 136–145. doi:10.5330/PSC.n.2012-16.136
Carey, J., Harrington, K., Martin, I., & Hoffman, D. (2012). A statewide evaluation of the outcomes of the implementation of ASCA National Model school counseling programs in rural and suburban Nebraska high schools. Professional School Counseling, 16, 100–107. doi:10.1177/2156759X0001600202
Carey, J., Harrington, K., Martin, I., & Stevenson, D. (2012). A statewide evaluation of the outcomes of the implementation of ASCA National Model school counseling programs in Utah high schools. Professional School Counseling, 16, 89–99. doi:10.1177/2156759X0001600203
Carey, J., Harrity, J., & Dimmitt, C. (2005). The development of a self-assessment instrument to measure a school district’s readiness to implement the ASCA National Model. Professional School Counseling, 8, 305–312.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Earlbaum Associates.
Cole, R. F., & Grothaus, T. (2014). A phenomenological study of urban school counselors’ perceptions of low-income families. Journal of School Counseling, 12(5).
Dimmitt, C., & Wilkerson, B. (2012). Comprehensive school counseling in Rhode Island: Access to services and student outcomes. Professional School Counseling, 16, 125–135. doi:10.1177/2156759X0001600205
Dodson, T. (2009). Advocacy and impact: A comparison of administrators’ perceptions of the high school counselor role. Professional School Counseling, 12, 480–487. doi:10.1177/2156759X0901200606
Farmer-Hinton, R. L., & McCullough, R. G. (2008). College counseling in charter high schools: Examining the opportunities and challenges. The High School Journal, 91(4), 77–90. doi:10.1353/hsj.0.0006
Fye, H. J., Miller, L. G., & Rainey, J. S. (2018). Predicting school counselors’ supports and challenges when implementing the ASCA National Model. Professional School Counseling, 21, 1–11. doi:10.1177/2156759X18777671
Gagnon, D. J., & Mattingly, M. J. (2016). Most U.S. school districts have low access to school counselors: Poor, diverse, and city school districts exhibit particularly high student-to-counselor ratios. Retrieved from University of New Hampshire Carsey School of Public Policy, Carsey Research: https://scholars.unh.edu/cgi/viewcontent.cgi?article=1285&context=carsey
Holcomb-McCoy, C. (2007). School counseling to close the achievement gap: A social justice framework for success. Thousand Oaks, CA: Corwin Press.
Lapan, R. T., Gysbers, N. C., & Sun, Y. (1997). The impact of more fully implemented guidance programs on the school experiences of high school students: A statewide evaluation study. Journal of Counseling & Development, 75, 292–302. doi:10.1002/j.1556-6676.1997.tb02344.x
Lapan, R. T., Gysbers, N. C., & Petroski, G. F. (2001). Helping seventh graders be safe and successful: A statewide study of the impact of comprehensive guidance and counseling programs. Journal of Counseling & Development, 79, 320–330. doi:10.1002/j.1556-6676.2001.tb01977.x
Lapan, R. T., Gysbers, N. C., Stanley, B., & Pierce, M. E. (2012). Missouri professional school counselors: Ratios matter, especially in high-poverty schools. Professional School Counseling, 16, 108–116. doi:10.1177/2156759X0001600207
Lapan, R. T., Whitcomb, S. A., & Aleman, N. M. (2012). Connecticut professional school counselors: College and career counseling services and smaller ratios benefit students. Professional School Counseling, 16, 117–124. doi:10.1177/2156759X0001600206
Lee, C. C. (2005). Urban school counseling: Context, characteristics, and competencies. Professional School Counseling, 8, 184–188.
Martin, I., & Carey, J. (2014). Development of a logic model to guide evaluations of the ASCA National Model for school counseling programs. The Professional Counselor, 4, 455–466. doi:10.15241/im.4.5.455
Martin, I., Carey, J., & DeCoster, K. (2009). A national study of the current status of state school
counseling models. Professional School Counseling, 12, 378–386.
doi:10.1177/2156759X0901200506
McCarthy, C., van Horn Kerne, V., Calfa, N. A., Lambert, R. G., & Guzmán, M. (2010). An exploration of school counselors’ demands and resources: Relationship to stress, biographic, and caseload characteristics. Professional School Counseling, 13, 146–158. doi:10.1177/2156759X1001300302
Mullen, P. R., Stevens, H., & Chae, N. (2019). School counselors’ attitudes toward evidence-based practices. Professional School Counselor, 22, 1–11. doi:10.1177/2156759X18823690
National Center for Education Statistics. (2018). Elementary/secondary information system. Retrieved from https://nces.ed.gov/ccd/elsi
Pallant, J. (2011). SPSS survival manual: A step by step guide to data analysis using SPSS (4th ed.). Buckingham, UK: Open University Press.
Pyne, J. R. (2011). Comprehensive school counseling programs, job satisfaction, and the ASCA National Model. Professional School Counseling, 15, 88–97. doi:10.1177/2156759X1101500202
Scafidi, J. (2013). The school staffing surge: Decades of employment growth in America’s public schools (Part II). Retrieved from https://files.eric.ed.gov/fulltext/ED543118.pdf
Schwartz, R. M., Schmidt, M. C., & Lose, M. K. (2012). Effects of teacher-student ratio in response to intervention approaches. The Elementary School Journal, 112, 547–567. doi:10.1086/664490
Sink, C. A. (2008). Elementary school counselors and teachers: Collaborators for higher student achievement. The Elementary School Journal, 108, 445–458. doi:10.1086/589473
Sink, C. A., Akos, P., Turnbull, R. J., & Mvududu, N. (2008). An investigation of comprehensive school counseling programs and academic achievement in Washington state middle schools. Professional School Counseling, 12, 43–53. doi:10.1177/2156759X0801200105
Sink, C. A., & Stroh, H. R. (2003). Raising achievement test scores of early elementary school students through comprehensive school counseling programs. Professional School Counseling, 6, 350–364.
Sutton, J. M., Jr., & Pearson, R. (2002). The practice of school counseling in rural and small town schools. Professional School Counseling, 5, 266–276.
U.S. Department of Education, Common Core of Data. (2016). Elementary/secondary information system [ElSi], 2015-2016 [Data set]. Retrieved from https://nces.ed.gov/ccd/elsi/tableGenerator.aspx
Villares, E., & Dimmitt, C. (2017). Updating the school counseling research agenda: A Delphi study. Counselor Education and Supervision, 56, 177–192. doi:10.1002/ceas.12071
Wilkerson, K., Pérusse, R., & Hughes, A. (2013). Comprehensive school counseling programs and student achievement outcomes: A comparative analysis of RAMP versus non-RAMP schools. Professional School Counseling, 16, 172–184. doi:10.1177/2156759X1701600302
Wright, W. (2012). The disparities between urban and suburban American education systems: A comparative analysis using social closure theory. Proceedings of The National Conference on Undergraduate Research (NCUR). Ogden, UT: Webster State University.
Young, A., & Kaffenberger, C. (2011). The beliefs and practices of school counselors who use data to implement comprehensive school counseling programs. Professional School Counseling, 15, 67–76. doi:10.1177/2156759X1101500204
Patrick R. Mullen, NCC, is an assistant professor at the College of William & Mary. Nancy Chae, NCC, is a doctoral candidate at the College of William & Mary. Adrienne Backer is a doctoral student at the College of William & Mary. Correspondence can be addressed to Patrick Mullen, P.O. Box 8795, Williamsburg, VA 23187-8795, prmullen@wm.edu
Mar 26, 2019 | Volume 9 - Issue 1
Tahani Dari, John M. Laux, Yanhong Liu, Jennifer Reynolds
A gap exists in the counseling profession between research and practice. Community-based participatory research (CBPR) is one approach that could reduce this gap. The CBPR framework can serve as an additional tool for translating research findings into practical interventions for communities and counseling practitioners. Stronger community partnerships between researchers and practitioners will further improve treatment for our clients. The purpose of this study was to develop competencies that would provide the foundations for a training guideline in CBPR. Using the Delphi method, an expert panel achieved consensus on 153 competencies (knowledge, skills, attitudes, activities). Competencies are significant for the profession because they establish best practice, guidelines of service, and professional training.
Keywords: community-based participatory research, research competencies, Delphi method, community partnerships, best practices
The counseling profession has a gap between research and practice (Guiffrida, Douthit, Lynch, & Mackie, 2011; Murray, 2009; Peterson, Hall, & Buser, 2016; Wester & Borders, 2014). Thirty percent of counseling practitioners fail to use academic counseling research findings in their clinical practice (Wester & Borders, 2014). Erford et al. (2011) conducted an 8-year analysis of the Journal of Counseling & Development (JCD) author affiliation and found that the number of articles published in the JCD by non-academically affiliated authors (e.g., in private practice, K–12 schools) declined from 10% in 2002 to 5% in 2008. This decline is even more precipitous considering that 31% of the JCD’s publications between 1978 and 1993 were contributed by non-academic authors (Weinrach, Lustig, Chan & Thomas, 1998). Erford et al. suggested that this drop may be caused by a decline in collaboration between scientists and practitioners or counselors. Woolf (2008) and Wester and Borders (2014) suggested that counselors are apathetic about research because they are unprepared to translate research findings into clinical practice. Further, according to Guiffrida et al. (2011), practitioners may view research to be irrelevant to their work and their clients’ needs. Peterson et al. (2016) indicated the gap may possibly exist between the research skills highlighted in counselor education and those applied in the field. Finally, Murray (2009) noted that researchers and counselors are disconnected from one another; therefore, research findings are not clearly and quickly disseminated to field-based counselors. Although the specific reasons for the researcher–practitioner disconnection vary among authors, there is a compelling need for counseling researchers and practitioners to work toward a common goal benefiting clients.
This gap comprises a problem for the profession because research should inform counselors’ clinical interventions and supervisors’ decisions (Lilienfeld, Ammirati, & David, 2012). When they do not, the gap between academic counseling researchers and counseling practitioners puts client well-being at risk. To provide the best outcomes for clients, counseling practitioners must be aware of and make use of current evidence-based treatments identified through academic research. Likewise, counseling researchers who fail to consider the clinical zeitgeist may promulgate lines of inquiry that are difficult to translate into clinical application. One way to minimize this gap is through stronger collaborations between academic counseling researchers and counseling practitioners who already serve clients in their communities. One rationale the authors offer is that although there might be a desire to collaborate, there are currently no agreed upon standards to establish parameters of those collaborations, making setting up partnerships more challenging for counseling researchers. Efforts to incorporate community-based participatory research (CBPR) approaches could further enhance treatment for clients by strengthening researcher–practitioner partnerships (Horowitz, Robinson, & Seifer, 2009).
Community-Based Participatory Research
CBPR (Israel, Eng, Schulz, & Parker, 2013) fosters partnerships between researchers, institutions, and communities (Lachance, Quinn, & Kowalski-Dobson, 2018; Poleshuck et al., 2018; Woods-Jaeger et al., 2018). CBPR is employed in conjunction with quantitative, qualitative, or mixed methods (Minkler & Wallerstein, 2008); serves as an additional tool for translating research findings into applicable clinical practice (Lightfoot, McCleary, & Lum, 2014; Minkler & Wallerstein, 2008); and improves communication between researchers and practitioners (Poleshuck et al., 2018).
CBPR rests on nine key principles that focus on the concept of cultural humility (Israel et al., 2013). Israel, Schulz, Parker, and Becker (1998) identified the first eight, which include the following principles:
(1) recognizes the community as a unit of identity; (2) builds on strengths and resources within the community; (3) facilitates collaborative partnerships in all phases of the research;
(4) integrates knowledge and action for mutual benefit of all partners; (5) promotes a co-learning and empowering process that attends to social inequalities; (6) involves a cyclical and iterative process; (7) addresses health from both positive and ecological perspectives; and
(8) disseminates findings and knowledge gained to all partners.” (pp. 178–180)
Minkler and Wallerstein (2008) added an important ninth CBPR principle: “(9) requires a long-term process and commitment to sustainability” (p. 11). Each of these principles relies on the researcher’s dedication to the tenet of cultural humility, which is critical to building improved relationships between researchers and communities founded upon increased trust, respect, and accountability.
Hook, Davis, Owen, Worthington, and Utsey (2013) defined cultural humility as appreciating one’s limitation with respect to what can be understood about another culture. It also is described as genuine concern for others, an absence of the power and dominance dynamic, a willingness to continue learning, an understanding of our own biases, and a dedication to self-reflection. Researchers who apply cultural humility tend to develop greater levels of trust, respect, and accountability within their communities, particularly with hard-to-reach communities. For example, Mannix, Austin, Baayd, and Simonsen (2018) utilized the principles of CBPR in their work with a Native American tribe and found that cultural training was the initial step toward community integration among researchers and the formation of equalizing partnerships. Sharing in one’s role as the expert and valuing co-learning helps to reframe the community as equal partners within the collaborative research process. Nonetheless, Collins et al. (2018) advocated that the CBPR approach can be employed in collaboration with diverse types of communities, involving, for example, police officers, health care workers, and business management.
CBPR’s benefits are well documented across disciplines (e.g., Collins et al., 2018; Green, 2007; Lightfoot et al., 2014; Lindamer et al., 2008; O’Brien et al., 2018; Yuan et al., 2016). These benefits include researchers’ ability to utilize research outcomes to advocate for clients (Gray & Price, 2014; Horowitz et al., 2009; McElfish et al., 2015), advance health disciplines (O’Fallon & Dearry, 2002; Israel et al., 2013), increase participant contributions (Case et al., 2014; Wagstaff, Graham, Farrell, Larkin, & Tatham, 2018), address multifaceted client issues (Corrigan, Pickett, Kraus, Burks, & Schmidt, 2015), improve mental health services (Case et al., 2014), and foster interprofessional relationships (Hergenrather, Geishecker, Clark, & Rhodes, 2013). Despite CBPR’s acceptance as a research tool and demonstrated benefits for increasing the effectiveness of researcher–practitioner communication, the counseling literature lacks counseling research specific to CBPR competency training guidelines.
The purpose of this study was to address this paucity by developing CBPR competency training guidelines. Consistent with the profession’s approach to competency development commonly seen in the profession (e.g., Ratts, Singh, Nassar-McMillan, Butler, & McCullough, 2016), the authors organized CBPR competencies into the following areas: knowledge, skills, attitudes, and activities. The development of CBPR competencies sets the stage for counseling research to become more understandable, accessible, and applicable to counselors and their communities, thus diminishing the gap between research and practice. Competencies are significant for the profession because they establish best practice, guidelines of service, and professional trainings (Toporek, Lewis, & Crethar, 2009).
Method
The authors employed the Delphi method to identify CBPR throughout the study. The Delphi method is an empirical approach that elicits expert opinion on research results and validation of content (Garson, 2013; Jorm, 2015; Ross, Kelly, & Jorm, 2014). It is an iterative process that progresses through consecutive survey rounds. This approach provides a reliable method for gathering structured expert insight to improve professional training and typically includes a minimum of two rounds (Garson, 2013). Experts’ responses are blinded to one another. Rigor and validity of the Delphi method relies on the knowledge and experience of an expert panel (Garson, 2013). There is no set number of experts that should serve on a Delphi panel, but researchers agree that a minimum of eight to 12 experts is sufficient and appropriate for Delphi studies (Novakowski & Wellar, 2008). The authors decided upon the Delphi method because we see it as the best model for identifying additional content not reflected in the current counseling literature for use in the development of a training guideline for counselors.
An online survey platform was used to collect data. Online survey tools can provide an effective means of conducting Delphi studies (Ross et al., 2014; Weise, Fisher, & Trollor, 2016). Online data collection techniques are economical for researchers and convenient for participants, especially when experts live apart geographically. These techniques provide anonymity and facilitate the equal inclusion of expert feedback where group dynamics might preclude such participation in a face-to-face setting (Garson, 2013).
Expert Panel Formation
According to Mead and Moseley (2001), establishing expertise, and by extension experts, is a context-based process that depends on a number of criteria, which may include their position, recognition by a stakeholder community, or established specialization. The prospective panel of experts was initially identified using a review of publication records (Garson, 2013), and augmented with the recommendations. The authors required that participant experts demonstrate both knowledge of and experience with carrying out CBPR. Twenty prospective expert participants were identified and recruited with an email that explained the nature of the study and contained a link to the Delphi study. CBPR is rarely found in the counseling literature; therefore, the authors also relied upon snowball sampling to recruit CBPR expert counselor educators (Jorm, 2015). Finally, the authors extended the invitation to participate to public health professionals with evidenced CBPR expertise, identifying them through a review of public health literature, where the CBPR framework originated and is now well established (Lightfoot et al., 2014; Minkler & Wallerstein, 2008). Moreover, counselors and public health professionals are similarly committed to advancing wellness among the communities they serve (Kaplan & Gladding, 2011). Of those 20 invited experts, 17 (85%) met the study’s inclusion criteria, which centered on relevant publications and knowledge of or professional experience with CBPR. Three (15%) indicated they were not qualified to participate. Another three declined to participate. The 14 remaining experts completed all facets of the Delphi study. Nine participants (64.3%) were identified through their publication records. The final five (35.7%) came from peers’ recommendations.
Eleven experts (78.6%) reported experience with CBPR in a university setting, eight (57.1%) in a non-profit organization, four (28.6%) in an agency setting, four (28.6%) in a health system (e.g., hospital, clinic), four (28.6%) in a K–12 school setting, one (7.1%) in a community-wide setting, and one (7.1%) in international projects. One expert (7.1%) did not identify a work setting. Five (35.7%) experts reported having more than 10 years of experience conducting CBPR research, including four with 18–21 years and one with 11 years of experience. Three (21.4%) stated that they had 4–5 years of experience, and another four (28.6%) reported 2–4 years of experience. One (7.1%) expert did not respond to the question. Thirteen experts (92.9%) listed their highest educational level as a PhD, and one expert (7.1%) indicated the highest degree was a master’s degree. Participants’ ages ranged from 30 to over 60 years. Four experts (28.6%) reported their age to be 30–39, two (14.3%) 40–49, seven (50%) 50–59, and one (7.1%) over 60. When asked to report their racial affiliation, 10 (71.4%) identified as European American, one (7.1%) as Hispanic, one (7.1%) as Asian/Pacific Islander, and two (14.3%) selected Other/Mixed. Finally, 10 identified as female (71.4%) and four identified as male (28.6%).
Procedure
Stage 1: Preparing items for the questionnaire. The authors conducted a literature review to compile content statements (Sivell, Lidstone, Taubert, Thompson, & Nelson, 2015) about the knowledge, skills, attitudes, and activities (competency domains) commonly used in CBPR. These content statements were used to create an online questionnaire for the Delphi study’s first round (Ross et al., 2014; Sivell et al., 2015; Weise et al., 2016).
Stage 2: Administer Round 1. The authors sent an email to the identified experts with a URL link to the study (Sivell et al., 2015). Experts then used a 5-point Likert scale response range to assess participants’ degree of agreement with each CBPR competency statement (Sivell et al., 2015; Vázquez-Ramos, Leahy, & Hernández, 2007). Additionally, experts provided their own answers to four open-ended survey questions that reflected the coding frame (i.e., competency domains) used in this study. Additional questions included: (1) What knowledge is required for counseling researchers to effectively carry out community-based participatory research? (2) What skills are considered essential for counseling researchers to carry out community-based participatory research? (3) What attitudes are essential for counseling researchers to develop community-based participatory research? and (4) What activities are necessary for counseling researchers to experience when engaging in community-based participatory research?
Stage 3: Prepare and administer Round 2. Next, the authors employed the qualitative content analysis software program, NVivo, to analyze the 161 statements that participants contributed. Statements about which the experts did not agree were removed. Round 2’s statements (n = 112) were solely those that were contributed to the open-ended questions posed to the experts in Round 1. The experts evaluated the revised questionnaire in the same manner as in Round 1.
Stage 4: Finalize competencies. The authors compiled the final list of competencies based on expert consensus. In accordance with other Delphi study practices (Keeney, Hasson, & McKenna, 2011; Weise et al., 2016), consensus was achieved when at least 70% of the experts either agreed or strongly agreed with the statement and the statement’s median score was 2.5 or lower. The authors chose to further strengthen consensus results by ensuring that a given statement also achieved an interquartile range (IQR) of less than or equal to 1 (Wester & Borders, 2014). Following Ross et al.’s (2014) suggestion, we sent a follow-up email with a final draft of the competencies to each participant. The email contained each of the final 153 statements (Appendix). The authors asked the participants to offer their final remarks about the statements and requested that they respond within a week and received no modifications.
Data Analysis
Descriptive quantitative analysis. The review of the Delphi process started upon the experts’ completion of Round 1 and was completed following Round 2. One part of the analysis involved quantitative feedback. SPSS was used to measure expert consensus. The data included frequency outputs on the percentage of overall responses to each statement, median, and IQR. According to Dalkey and Helmer (1963), the median response for each statement is a central statistic involved in Delphi processes. IQR is a measure of variability that is less susceptible to outliers than the range. IQR allowed the authors to further increase objectivity and rigor in the validating process to determine final expert statements (Wester & Borders, 2014). IQR also allowed researchers to assess the variability in responses. An IQR of less than or equal to 1 on a 5-point Likert scale indicates a low variability in responses, whereas a score greater than 1 signifies a higher range of variability.
Content analysis. Participants’ contributed statements were used to enhance the level of expert consensus with the follow-up questionnaire. The researchers conducted a qualitative content analysis (QCA) for these contributions (Weise et al., 2016). The QCA clearly and systematically categorized statements within the range of the study’s nine CBPR principles. Using NVivo, the authors coded the experts’ statements using the domains of the theoretical coding framework (Schreier, 2012): knowledge, attitudes, skills, and activities. The authors then assigned each of the frame-coded statements to one of the nine CBPR principles.
Results
The results from Round 1 and Round 2 are presented in the Appendix. A total of 64 statements were omitted between Rounds 1 and 2 because they either did not reach consensus (meeting all three criteria) or represented a repeated item. Of the final 153 competencies, 49 relate to the knowledge domain, 43 relate to the attitudes domain, 31 relate to the skills domain, and 25 relate to the activities domain. These statements were further subcategorized according to the nine CBPR principles (P1–P9) or themes that emerged from the content analysis: 15 statements were related to P1, 12 statements were related to P2, 25 statements were related to P3, 28 statements were related to P4, 18 statements were related to P5, 12 statements were related to P6 and P7, seven statements were related to P8, and 14 statements were related to P9.
Certain statements did not fit within the nine CBPR principles. Additionally, there were statements that seemed to fit within multiple categories. Some themes that the authors did not expect emerged from the open-ended responses. These included seven statements related to core traits and three statements related to mentoring, which are also presented in the Appendix. The following discussion will further describe the results.
Discussion
The aim of the study was to develop competencies that emphasize knowledge, skills, attitudes, and activities that would provide the foundations for a training guideline in CBPR for the counseling profession. A growing number of counseling researchers highlight researcher and community collaboration (Bryan, 2009; Guiffrida et al., 2011; Wester & Borders, 2014); however, comprehensive training guidelines that outline the competencies required to foster such partnerships do not exist in the counseling literature. We argue that by providing access to this emerging approach to building researcher–community partnerships within the community (particularly practitioners), the clients/communities’ well-being will be enhanced. CBPR emerged in recent years as the most promising researcher–community approach to research (Lawson, Caringi, Pyles, Jurkowski, & Bozlak, 2015; Lightfoot et al., 2014). The CBPR competencies identified through this study could provide further guidance to researchers for building these relationships in the community. Researchers that advocate for researcher–practitioner partnerships emphasize their potential for advancing treatment for clients (Teachman et al., 2012). These partnerships improve communication and allow research findings to be translated into more practical interventions. We anticipate that by offering a standardized approach for a training guide to fostering researcher–community partnerships, future counseling researchers will receive more consistent and effective training in CBPR practices.
CBPR Competencies
Consistent with previous literature, all 14 experts agreed that CBPR is about relationships and relationship building. They further allowed that a CBPR framework fosters conversations between partners within the community. The experts also endorsed CBPR as a complementary, not competing, approach to research. Although the results of this study confirm the necessary knowledge components of the CBPR framework, they move beyond making the argument that CBPR is a necessary practice, demonstrating how researchers might effectively implement such practices. Thus, we offer key insights from the remaining categories understood as necessary for competency in a given practice (Toporek et al., 2009) with the aim of identifying best practices and means of implementation for community partnerships. Competency in this framework will enhance methodological choices made by researchers and their partner communities. The following section highlights statements categorized by domain with high expert consensus (100% of the expert panel indicated they either strongly agree or agree).
Knowledge. All experts agreed that the knowledge required for counseling researchers to effectively carry out CBPR includes understanding that the term “CBPR Researchers” applies to both academic and community partners (extended to counseling practitioners). Experts also agreed that academic CBPR researchers need to know or be willing to learn about the community’s issues, concerns, and strengths. When researchers include community partners in the research process, it helps to develop trust and respect between these two groups and potentially leads to a deeper interpretation of the findings. Likewise, experts acknowledged the importance of inviting community partners to participate in dissemination of research findings. Finally, CBPR can be effective in bringing community partners together to determine priorities.
Skills. The experts agreed that practicing CBPR requires effective and reflective listening skills, group facilitation skills, and the ability to create strong partnerships (e.g., negotiating, collaborating, networking, liaising). Researchers should practice cultural humility and be willing to work across the varying needs of communities with different cultures and identities. Therefore, researchers can help community partners recognize the strengths and resources already embedded in the current structure of their own communities. Finally, the experts agreed that CBPR researchers should communicate findings in ways that make skillful use of technology and are concise, clear, and appropriate so that the community may participate in the interpretation of results.
Attitudes. The experts identified cultural humility, flexibility, and persistence as essential CBPR attitudes. This required that researchers share power—for example, implementing shared decision-making in their projects with their community partners. It is imperative that researchers recognize that every community has its own unique strengths. Likewise, CBPR researchers make a commitment to collaboration by sharing expertise, being accountable, and giving credit to their community partners for their contributions to knowledge production. This entails researchers valuing power sharing with their community partners, including shared decision-making in their projects, while still upholding scientific rigor. Moving beyond shared decision-making, CBPR researchers also recognize the importance of working together to find innovative ways of disseminating research results. At times, researchers will need to commit to building continued relationships and networks within the community beyond a particular project or funding phase.
Activities. Finally, the findings confirm that carrying out CBPR necessitates particular experiences for counseling researchers. For instance, experts agreed that in order to foster effective partnerships, they need to practice deep listening and undertake participant observation at many different stages of their research. Other activities that experts consistently agreed were integral to the CBPR approach include frequent meetings, spending in-depth time getting to know the community, and collecting and analyzing data in collaboration with community partners. Counseling researchers commit to inviting community partners to participate throughout the research process, including organizing and planning meetings, data collection, data interpretation, findings dissemination, and even training or mentoring in research methods. All of these activities require a willingness to be educated about the community by the community members during the CBPR process.
Implications for Counseling Practice and Counselor Education
The CBPR competencies developed in this study serve to foster relationships between researchers and counseling practitioners in the community. Through these relationships, researchers, practitioners, and the communities they represent can work to reduce the gap between research and practice through enhanced community–researcher communication (Teachman et al., 2012; Wagstaff et al., 2018) and the translation of research outcomes into counseling practice (Wester & Borders, 2014). One aim of identifying the CPBR competencies was to provide mentoring to community partners, particularly counseling practitioners, on how to use research results to create effective community interventions. The goal is to close the gap between research and practice to improve treatment for our clients and improve communities.
A common language for interprofessional collaboration. This study brought together experts from two key fields whose efforts resulted in 153 competency statements that reflect the knowledge, skills, attitudes, and activities necessary to successfully carry out CBPR research. These CBPR competencies provide researchers with a vehicle to facilitate interprofessional work toward a common vision of community well-being. For instance, all experts on the panel for the present study agreed that CBPR researchers understand that when the community puts forth a common effort and agrees on common goals, trusting relationships are established, leading to enhanced social networks and better use of resources. Thus, community–researcher partnership outcomes include the enhancement of access to, delivery, and quality of mental health services for communities (Collins et al., 2018), particularly hard-to-reach communities (Brookman-Frazee et al., 2016; Nieweglowski et al., 2018; O’Brien et al., 2018), and culturally appropriate interventions (Cox, 2017; Doll & Brady, 2013). Community-based research can facilitate efforts geared toward increasing the relevance of intervention methods.
Identifying competencies for training and proficiency in CBPR. The CBPR competencies identified in this study can serve as the basis for developing a training guideline for counseling practitioners, counselor–researchers, and counselors-in-training. Such a guideline allows stakeholders to maintain awareness of current and emerging research practices such as CBPR and enhances their professional responsibility (American Counseling Association, 2014, Standard C.2.f; Council for Accreditation of Counseling and Related Educational Programs, 2015, Section 6.4.d). Identifying competencies for training and proficiency is one approach to curriculum development (Mason & Schwartz, 2012) that we believe can be particularly effective. This study not only identified the necessary competencies for best practices in CBPR, but organized the competencies into meaningful categories that pertain to the four critical domains of proficiency in a given practice: knowledge, skills, attitudes, and activities. The sequence we have provided can be a useful map to the nine principles of the CBPR approach. This study lays a foundation for an effective training guideline that highlights how each CBPR domain builds upon the next. Having a CBPR training guideline will help standardize best practices in the collaborative process, thus enhancing researcher–practitioner engagement.
Promoting experiential learning opportunities for students. Counselor educators can connect emergent research and experiential learning in their curricula. The competencies highlighted by the current study may support project-based learning activities in courses that require students to approach community members and partake in a collaborative endeavor. The expectation is that the CBPR competencies would provide counselor educators and counselors-in-training with standardized guidelines for best practice in community-based research that they can apply when ready to pursue a project of their own. The emphasis in this case would be to prepare future counselors for community–researcher partnerships. The benefit of engaging students at the training level in CBPR research through the use of these competencies is that it exposes students to an awareness of the collaborative process by moving beyond knowledge components and learning the skills, attitudes, and activities necessary to initiate a partnership. This could require that a project be spread out over two or three semesters as a component in a field-based practicum or internship. The competencies can be used to structure such courses as well. For example, course objectives for one semester’s internship might include the knowledge, skills, attitudes, and actions aimed at principles one, two, and three, whereas another semester may cover principles four, five, six, and so on. Alternatively, counselor educators might choose to design their research projects through interdisciplinary or interprofessional collaborations across campus that account for CBPR principles (McElfish et al., 2015; Talley & Williams, 2018), which students may be able to join as a component of training.
Limitations of the Study
One limitation of the study reflects the emergent nature of CBPR approaches in the counseling literature, which is that some CBPR researchers may be limited in their years of formal experience with the practice. For instance, four of the expert participants reported having less than four years of experience conducting CBPR projects. Although years of experience can be an important factor in attributing expertise, several studies have also highlighted that expert status is contingent upon many contextual factors, including recognition by other experts and stakeholders (Mead & Moseley, 2001). In this case, because CBPR is still a new practice in counseling research, peer recommendation was an identifying factor.
Another limitation of this study is the number of rounds conducted. Typically, a Delphi study will include two to eight rounds, with three as the median (Garson, 2013). The aim of the third round typically involves experts providing additional feedback about the items. Although we initiated a third round of the study, experts had little to no feedback to offer, meaning that the final statements were accepted with minimum revision. Although the authors interpreted this lack of feedback as validation of the final outcomes, one might otherwise argue that the lack of feedback better reflects other factors such as expert availability and time.
Suggestions for Future Research
We suggest that future researchers apply the Rasch model to the results of the Delphi study in order to test whether or not the competencies can be quantified in a meaningful way (Bond & Fox, 2015). The main question is whether the structure of the construct is qualitative or quantitative. If quantitative, then the Rasch model will unveil the extent to which the competency statements fall on a continuum. If they do not, that does not undermine the meaningfulness of the Delphi work or the content therein; rather, it would provide evidence that the competencies have a qualitative structure, and descriptive statistics are more appropriate for summarizing responses to them.
If the competencies can form a quantitative linear variable, then validating the results from this Delphi study against further measures will help the researchers translate the competencies into an assessment tool, where it is justifiable to sum up responses, report a total score, and perform statistical analyses. This assessment tool could then be used to identify and assess the counselors’ own knowledge, skills, attitudes, and activities toward using the CBPR approach in a quantifiable way. Thus, the Rasch model is not an alternative to the Delphi study. Rather, it is a model that can test the extent to which it is justifiable to transform the statements gathered through the Delphi model into measurable variables; strengthening the efficacy of the competency statements guides instrument development to strengthen the results. Under the Rasch model, researchers can pilot the competency items to the counselors, who can be understood as the consumers of the instrument, and not to the experts who developed the competencies.
Conclusion
In conclusion, the results of the study provide an outline of evidence-based competencies derived from an empirical Delphi method that combined a wide-ranging literature review with expert feedback. This study comprises the beginning stages of the development and validation of CBPR competencies in counseling that may be utilized for training, practice, and further research. The findings of the present study provide awareness and initial competencies necessary to carry out CBPR research. Finally, the authors consider increasing the number of researcher–community partnerships to be key in bridging the gap between scientists and practitioners and advancing the profession. Ultimately, the aim is to improve the well-being of our clients and communities.
Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.
References
American Counseling Association. (2014). 2014 ACA code of ethics. Alexandria, VA: Author.
Bond, T. G., & Fox, C. M. (2015). Applying the Rasch Model: Fundamental measurement in the human sciences (3rd ed.). New York, NY: Routledge.
Brookman-Frazee, L., Stahmer, A., Stadnick, N., Chlebowski, C., Herschell, A., & Garland, A. F. (2016). Characterizing the use of research-community partnerships in studies of evidence-based interventions in children’s community services. Administration and Policy in Mental Health and Mental Health Services Research, 43, 93–104. doi:10.1007/s10488-014-0622-9
Bryan, J. (2009). Engaging clients, families, and communities as partners in mental health. Journal of Counseling & Development, 87, 507–511. doi:10.1002/j.1556-6678.2009.tb00138.x
Case, A. D., Byrd, R., Claggett, E., DeVeaux, S., Perkins, R., Huang, C., . . . Kaufman, J. S. (2014). Stakeholders’ perspectives on community-based participatory research to enhance mental health services. American Journal of Community Psychology, 54, 397–408.
doi:10.1007/s10464-014-9677-8
Collins, S. E., Clifasefi, S. L., Stanton, J., The LEAP Advisory Board, Straits, K. J. E., Gil-Kashiwabara, E., . . . Wallerstein, N. (2018). Community-based participatory research (CBPR): Towards equitable involvement of community in psychology research. American Psychologist, 73, 884–898. doi:10.1037/amp0000167
Corrigan, P., Pickett, S., Kraus, D., Burks, R. & Schmidt, A. (2015). Community-based participatory research examining the health care needs of African Americans who are homeless with mental illness. Journal of Health Care for the Poor and Underserved, 26, 119–133. doi:10.1353/hpu.2015.0018
Council for Accreditation of Counseling and Related Educational Programs. (2015). 2016 standards for accreditation. Alexandria, VA: Author.
Cox, R. B., Jr. (2017). Promoting resilience with the ¡Unidos se puede! program: An example of translational research for Latino families. Family Relations, 66, 712–728. doi:10.1111/fare.12265
Dalkey, N. C., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9, 458–467.
Doll, J., & Brady, K. (2013). Project HOPE: Implementing sensory experiences for suicide prevention in a Native American community. Occupational Therapy in Mental Health, 29, 149–158.
doi:10.1080/0164212X.2013.788977
Erford, B. T., Miller, E. M., Schein, H., McDonald, A., Ludwig, L., & Leishear, K. (2011). Journal of Counseling & Development publication patterns: Author and article characteristics from 1994 to 2009. Journal of Counseling & Development, 89, 73–80.
doi:10.1002/j.1556-6678.2011.tb00062.x
Garson, G. D. (2013). The Delphi method in quantitative research. Asheboro, NC: Statistical Associates Publishers.
Gray, L. A., & Price, S. K. (2014). Partnering for mental health promotion: Implementing evidence based mental health services within a maternal and child home health visiting program. Clinical Social Work Journal, 42, 70–80. doi:10.1007/s10615-012-0426-x
Green, L. W. (2007). The prevention research centers models of practice-based evidence. American Journal of Preventive Medicine, 33, S6–S8. doi:10.1016/j.amepre.2007.03.012
Guiffrida, D. A., Douthit, K. Z., Lynch, M. F., & Mackie, K. L. (2011). Publishing action research in counseling journals. Journal of Counseling & Development, 89, 282–287.
doi:10.1002/j.1556-6678.2011.tb00090.x
Hergenrather, K. C., Geishecker, S., Clark, G., & Rhodes, S. D. (2013). A pilot test of the HOPE intervention to explore employment and mental health among African American gay men living with HIV/AIDS: Results from a CBPR study. AIDS Education and Prevention, 25, 405–422. doi:10.1521/aeap.2013.25.5.405
Hook, J. N., Davis, D. E., Owen, J., Worthington, E. L., & Utsey, S. O. (2013). Cultural humility: Measuring openness to culturally diverse clients. Journal of Counseling Psychology, 60, 353–366. doi:10.1037/a0032595
Horowitz, C. R., Robinson, M., & Seifer, S. (2009). Community-based participatory research from the margin to the mainstream: Are researchers prepared? Circulation, 119, 2633–2642.
doi:10.1161/CIRCULATIONAHA.107.729863
Israel, B. A., Eng, E., Schulz, A. J., & Parker, E. A. (Eds.). (2013). Methods for community-based participatory research for health (2nd ed.). San Francisco, CA: Jossey-Bass.
Israel, B. A., Schulz, A. J., Parker, E. A., & Becker, A. B. (1998). Review of community-based research: Assessing partnership approaches to improve public health. Annual Review of Public Health, 19, 173–202. doi:10.1146/annurev.publhealth.19.1.173
Jorm, A. F. (2015). Using the Delphi expert consensus method in mental health research. Australian and New Zealand Journal of Psychiatry, 49, 887–897. doi:10.1177/0004867415600891
Kaplan, D. M., & Gladding, S. T. (2011). A vision for the future of counseling: The 20/20 principles for unifying and strengthening the profession. Journal of Counseling & Development, 89, 367–372. doi:10.1002/j.1556-6678.2011.tb00101.x
Keeney, S., Hasson, F., & McKenna, H. (2011). The Delphi technique in nursing and health research. Chichester, UK: Wiley-Blackwell.
Lachance, L., Quinn, M., & Kowalski-Dobson, T. (2018). The food & fitness community partnerships: Results from 9 years of local systems and policy changes to increase equitable opportunities for health. Health Promotion Practice, 19, 92S–114S. doi:10.1177/1524839918789400
Lawson, H. A., Caringi, J. C., Pyles, L., Jurkowski, J. M., & Bozlak, C. T. (2015). Participatory action research. New York, NY: Oxford University Press.
Lightfoot, E., McCleary, J. S., & Lum, T. Y. (2014). Asset mapping as a research tool for community-based participatory research in social work. Social Work Research, 38, 59–64. doi:10.1093/swr/svu001
Lilienfeld, S. O., Ammirati, R., & David, M. (2012). Distinguishing science from pseudoscience in school psychology: Science and scientific thinking as safeguards against human error. Journal of School Psychology, 50, 7–36. doi:10.1016/j.jsp.2011.09.006
Lindamer, L. A., Lebowitz, B. D., Hough, R. L., Garcia, P., Aquirre, A., Halpain, M. C., & Jeste, D. V. (2008). Public-academic partnerships: Improving care for older persons with schizophrenia through an academic-community partnership. Psychiatric Services, 59, 236–239. doi:10.1176/ps.2008.59.3.236
Mannix, T. R., Austin, S. D., Baayd, J. L., & Simonsen, S. E. (2018). A community needs assessment of urban Utah American Indians and Alaska Natives. Journal of Community Health, 43, 1217–1227.
doi:10.1007/s10900-018-0542-9
Mason, R., & Schwartz, B. (2012). Using a Delphi method to develop competencies: The case of domestic violence. Journal of Community Medicine & Health Education, 2(2), 124. doi:10.4172/2161-0711.1000124
McElfish, P. A., Kohler, P., Smith, C., Warmack, S., Buron, B., Hudson, J., . . . Rubon-Chutaro, J. (2015). Community-driven research agenda to reduce health disparities. CTS: Clinical & Translational Science, 8, 690–695. doi:10.1111/cts.12350
Mead, D., & Moseley, L. (2001). The use of Delphi as a research approach. Nurse Researcher, 8(4), 4–23. doi:10.7748/nr2001.07.8.4.4.c6162
Minkler, M., & Wallerstein, N. (Eds.). (2008). Community-based participatory research for health: From process to outcomes (2nd ed.). San Francisco, CA: Jossey-Bass.
Murray, C. E. (2009). Diffusion of innovation theory: A bridge for the research-practice gap in counseling. Journal of Counseling & Development, 87, 108–116.
doi:10.1002/j.1556-6678.2009.tb00556.x
Nieweglowski, K., Corrigan, P. W., Tyas, T., Tooley, A., Dubke, R., Lara, J., . . . The Addiction Stigma Research Team. (2018). Exploring the public stigma of substance use disorder through community-based participatory research. Addiction Research & Theory, 26, 323–329. doi:10.1080/16066359.2017.1409890
Novakowski, N., & Wellar, B. (2008). Using the Delphi technique in normative planning research: Methodological design considerations. Environmental and Planning A: Economy and Space, 40, 1485–1500. doi:10.1068/a39267
O’Brien, M. A., Lofters, A., Wall, B., Pinto, A. D., Elliott, R., Pietrusiak, M.-A., . . . Paszat, L. (2018). Better Health Durham: Community engagement in a cluster RCT of a prevention practitioner intervention in low-income neighborhoods. Journal of Global Oncology, 22s. doi:10.1200/jgo.18.53100
O’Fallon, L. R., & Dearry, A. (2002). Community-based participatory research as a tool to advance environmental health sciences. Environmental Health Perspectives, 110(S2), 155–159.
doi:10.1289/ehp.02110s2155
Peterson, C. H., Hall, S. B., & Buser, J. K. (2016). Research training needs of scientist-practitioners: Implications for counselor education. Counselor Education and Supervision, 55(2), 80–94. doi:10.1002/ceas.12034
Poleshuck, E., Mazzotta, C., Resch, K., Rogachefsky, A., Bellenger, K., Raimondi, C., . . . Cerulli, C. (2018). Development of an innovative treatment paradigm for intimate partner violence victims with depression and pain using community-based participatory research. Journal of Interpersonal Violence, 33, 2704–2724. doi:10.1177/0886260516628810
Ratts, M. J., Singh, A. A., Nassar-McMillan, S., Butler, S. K., & McCullough, J. R. (2016). Multicultural and social justice counseling competencies: Guidelines for the counseling profession. Journal of Multicultural Counseling and Development, 44, 28–48. doi:10.1002/jmcd.12035
Ross, A. M., Kelly, C. M., & Jorm, A. F. (2014). Re-development of mental health first aid guidelines for suicidal ideation and behaviour: A Delphi study. BMC Psychiatry, 14, 241.
doi:10.1186/s12888-014-0241-8
Schreier, M. (2012). Qualitative content analysis in practice. London, UK: SAGE Publications.
Sivell, S., Lidstone, V., Taubert, M., Thompson, C., & Nelson, A. (2015). Identifying the key elements of an education package to up-skill multidisciplinary adult specialist palliative care teams caring for young adults with life-limiting conditions: An online Delphi study. BMJ Supportive & Palliative Care, 5, 306–315. doi:10.1136/bmjspcare-2013-000595
Talley, C. H., & Williams, K. P. (2018). Preparing future healthcare professionals for community engagement: A course-based research experience. ABNF Journal, 29(2), 33–41.
Teachman, B. A., Drabick, D. A., Hershenberg, R., Vivian, D., Wolfe, B. E., & Goldfried, M. R. (2012). Bridging the gap between clinical research and clinical practice: Introduction to the special section. Psychotherapy (Chicago, Ill.), 49(2), 97–100. doi:10.1037/a0027346
Toporek, R. L., Lewis, J. A., & Crethar, H. C. (2009). Promoting systemic change through the ACA advocacy competencies. Journal of Counseling & Development, 87, 260–268. doi:10.1002/j.1556-6678.2009.tb00105.x
Vázquez-Ramos, R., Leahy, M., & Hernández, N. E. (2007). The Delphi method in rehabilitation counseling research. Rehabilitation Counseling Bulletin, 50, 111–118. doi:10.1177/00343552070500020101
Wagstaff, C., Graham, H., Farrell, D., Larkin, M., & Tatham, L. (2018). Perspectives of cannabis use in the life experience of men with schizophrenia. International Journal of Mental Health Nursing, 27, 1099–1108. doi:10.1111/inm.12422
Weinrach, S. G., Lustig, D., Chan F., & Thomas, K. R. (1998). Publication patterns of The Personnel and Guidance Journal/Journal of Counseling & Development: 1978 to 1993. Journal of Counseling & Development, 76, 427–435. doi:10.1002/j.1556-6676.1998.tb02701.x
Weise, J., Fisher, K. R., & Trollor, J. (2016). Utility of a modified online Delphi method to define workforce competencies: Lessons from the Intellectual Disability Mental Health Core competencies project. Journal of Policy and Practice in Intellectual Disabilities, 13, 15–22. doi:10.1111/jppi.12142
Wester, K. L., & Borders, L. D. (2014). Research competencies in counseling: A Delphi study. Journal of Counseling & Development, 92, 447–458. doi:10.1002/j.1556-6676.2014.00171.x
Woods-Jaeger, B. A., Sexton, C. C., Gardner, B., Siedlik, E., Slagel, L., Tezza, V., & O’Malley, D. (2018). Development, feasibility, and refinement of a toxic stress prevention research program. Journal of Child & Family Studies, 27, 3531–3543. doi:10.1007/s10826-018-1178-1
Woolf, S. H. (2008). The meaning of translational research and why it matters. JAMA, 299, 211–213.
doi:10.1001/jama.2007.26
Yuan, N. P., Gaines, T. L., Jones, L. M., Rodriguez, L. M., Hamilton, N., & Kinnish, K. (2016). Bridging the gap between research and practice by strengthening academic-community partnerships for violence research. Psychology of Violence, 6, 27–33. doi:10.1037/vio0000026
Appendix
Final CBPR Competencies (Round 1 and Round 2 Results)
| Domain |
Sub-Category |
Statement |
% |
Md |
IQR |
|
|
Round One (Statements: Review of the Literature) |
|
|
|
| K |
P1 |
S.2 CBPR partnerships define the parameters of community |
78.6 |
2.00 |
0.50 |
| K |
P1 |
S.3 Community could be described as geographic entity, a group that shares a common vision and/or identity |
78.6 |
2.00 |
0.75 |
| A |
P1 |
S.4 CBPR is a research orientation, rather than a method, that aims at building community partnerships |
92.9 |
2.00 |
1.00 |
| A |
P1 |
S.6 CBPR researchers must recognize the limits of their knowledge about their community partners |
85.7 |
1.00 |
1.00 |
| A |
P1 |
S.7 CBPR researchers should work toward cultural competency |
85.7 |
1.00 |
1.00 |
| A |
P1 |
S.8 CBPR researchers should value cultural humility |
92.9 |
1.00 |
1.00 |
| S |
P1 |
S.9 CBPR researchers need to acquire appropriate tools and approaches for interacting with community partners |
100 |
1.00 |
1.00 |
| S |
P1 |
S.10 CBPR researchers must be capable of negotiating and consulting with potential community partners |
100 |
1.00 |
1.00 |
| S |
P1 |
S.12 CBPR researchers need to be skilled at problem solving that might arise when making decisions and negotiating |
92.9 |
2.00 |
1.00 |
| K |
P2 |
S.16 CBPR researchers strive to recognize and develop on assets and relations presently within the community |
100 |
1.00 |
1.00 |
| K |
P2 |
S.18 CBPR researchers understand that when the community puts forth a common effort and agrees on common goals, trust is established, which leads to enhanced social networks/relationships and better implementation of resources |
92.9 |
2.00 |
0.00 |
| A |
P2 |
S.19 Every community has its own unique strengths |
100 |
1.00 |
0.00 |
| A |
P2 |
S.20 CBPR frameworks foster conversations between partners within the community |
100 |
1.00 |
1.00 |
| S |
P2 |
S.21 CBPR approaches also help community partners recognize the strengths and resources already embedded within the current structure of their own community |
100 |
1.00 |
1.00 |
| S |
P2 |
S.22 CBPR researchers must acquire an ability to identify community assets within the community |
92.9 |
1.00 |
1.00 |
| AC |
P2 |
S.24 CBPR researchers will engage with the community in order to learn more about what resources are already available within the community |
92.9 |
1.00 |
0.25 |
| K |
P3 |
S.26 CBPR approaches aim to level the power differences between researchers and community partners by having them engage in an equal partnership |
92.9 |
1.00 |
1.00 |
| K |
P3 |
S.27 CBPR researchers encourage and invite community partners to engage in each research phase |
92.9 |
1.00 |
1.00 |
| K |
P3 |
S.28 Researchers and community partners should co-analyze and co-interpret research results |
100 |
2.00 |
1.00 |
| K |
P3 |
S.29 When community partners are involved in the research process, deeper interpretation of findings may occur |
100 |
1.00 |
1.00 |
| A |
P3 |
S.30 CBPR researchers make a commitment to collaboration by sharing expertise, being accountable, and giving credit to their communities’ partners for their contributions to knowledge production |
100 |
1.00 |
0.25 |
| A |
P3 |
S.31 CBPR researchers recognize the value of sharing power with community partners |
100 |
1.00 |
1.00 |
| A |
P3 |
S.32 CBPR researchers are flexible and accommodating |
92.9 |
1.00 |
1.00 |
| S |
P3 |
S.33 CBPR researchers must be persistent and tolerant, especially when faced with obstacles in the research plan or environment |
85.7 |
1.00 |
0.25 |
| S |
P3 |
S.34 CBPR researchers must be able to collaborate with community partners in the interpretation of results |
100 |
1.00 |
1.00 |
| S |
P3 |
S.35 Facilitate interpretation of results into practice |
92.9 |
1.50 |
1.00 |
| S |
P3 |
S.37 CBPR researchers must be willing to mentor community partners to develop skills in participating in the research project |
92.9 |
1.00 |
1.00 |
| AC |
P3 |
S.38 CBPR researchers create time for reflection and self-awareness |
85.7 |
1.00 |
1.00 |
| AC |
P3 |
S.39 CBPR researchers schedule meetings with community partners to converse and clarify viewpoints of stress/difficulties encountered |
100 |
1.00 |
1.00 |
| AC |
P3 |
S.40 CBPR researchers provide community partners the opportunity to be part of the research project from start to finish |
100 |
1.00 |
1.00 |
| K |
P4 |
S.41 CPBR starts with a research area that is significant to the community |
92.9 |
1.00 |
1.00 |
| K |
P4 |
S.42 The CBPR research agenda is co-developed with community partners |
92.9 |
1.00 |
1.00 |
| K |
P4 |
S.43 CBPR encourages community partners to identify local impediments/barriers and unite community assets to work toward community well-being |
92.9 |
1.50 |
1.00 |
| K |
P4 |
S.44 Including community partners in the research process helps develop trust and respect between researchers and community |
100 |
1.00 |
1.00 |
| A |
P4 |
S.45 CBPR researchers value the contributions of community partners |
100 |
1.00 |
0.00 |
| A |
P4 |
S.46 CBPR researchers encourage community partners to share vital perspectives and beliefs |
100 |
1.00 |
0.00 |
| A |
P4 |
S.47 CBPR researchers welcome shared responsibilities in the research process |
100 |
1.00 |
1.00 |
| A |
P4 |
S.48 CBPR researchers are prepared to learn about the community through the lens of the community partner |
92.9 |
1.00 |
0.00 |
| A |
P4 |
S.49 CBPR researchers respect local knowledge and cultural perspectives |
100 |
1.00 |
0.00 |
| S |
P4 |
S.50 CBPR researchers practice deep listening in order to learn from their community partner |
100 |
1.00 |
1.00 |
| S |
P4 |
S.51 Effective communication and management skills are critical to engaging with community stakeholders to form partnerships |
100 |
1.00 |
1.00 |
| S |
P4 |
S.52 CBPR researchers are willing to negotiate and make compromises with community partner |
100 |
1.00 |
1.00 |
| AC |
P4 |
S.54 CBPR researchers engage in consistent and open communication |
100 |
1.00 |
0.25 |
| AC |
P4 |
S.56 CBPR researchers educate community partners on the research process |
92.9 |
1.50 |
1.00 |
| AC |
P4 |
S.57 CBPR researchers and community partners must make a joint effort to decide upon task roles and allocate time for future meetings: a consistent two-way communication |
85.7 |
2.00 |
1.00 |
| K |
P5 |
S.58 CBPR researchers aspire to promote science while at the same time providing local interventions/strategies to attend to local matters in the community |
92.9 |
1.50 |
1.00 |
| K |
P5 |
S.59 CBPR integrates knowledge and action intending to enhance community well-being |
92.9 |
1.00 |
1.00 |
| K |
P5 |
S.60 CBPR researchers should include the interpretation of research results into practice, thus benefiting community partners |
85.7 |
1.00 |
1.00 |
| A |
P5 |
S.61 One aim of CBPR is to provide mentoring to community partners on how to use research results in order to create effective community interventions |
92.9 |
2.00 |
1.00 |
| A |
P5 |
S.62 CBPR researchers understand that this framework does not require researchers to give up scientific rigor |
100 |
1.00 |
1.00 |
| S |
P5 |
S.63 CBPR researcher must know how to demonstrate the direct benefits of the research project to community partners |
85.7 |
1.00 |
1.00 |
| AC |
P5 |
S.64 CBPR researcher will assist community partners in developing interventions/programs based on research findings |
85.7 |
1.00 |
1.00 |
| K |
P6 |
S.65 CBPR researchers attend to issues that are of importance to the community partners involved |
100 |
1.00 |
0.00 |
| K |
P6 |
S.66 The CBPR approach stresses the environmental influences that can cause health issues such as social, economic, cultural, and historical and political realms |
92.9 |
1.00 |
1.00 |
| K |
P6 |
S.68 CBPR researchers attend to physical, mental, and social well-being, taking into account individual, family, and community contexts |
92.9 |
1.00 |
1.00 |
| S |
P6 |
S.70 CBPR researchers possess advocacy skills to bring awareness to community partners and/or other stakeholders of the contributing factors of health problem |
85.7 |
2.00 |
1.00 |
| S |
P6 |
S.71 CBPR researchers gather data from multiple sources to assess community priority issues |
85.7 |
1.00 |
1.00 |
| AC |
P6 |
S.72 CBPR researchers and community partners problem-solve and take a course of action to reduce disparities in the community |
92.9 |
1.00 |
1.00 |
| K |
P7 |
S.76 CBPR can be effective in bringing community partners together to determine priorities |
100 |
1.00 |
1.00 |
| A |
P7 |
S.79 CBPR researchers are persistent and flexible |
100 |
1.50 |
1.00 |
| A |
P7 |
S.80 CBPR researchers are prepared for further collaboration than initially anticipated, depending on community needs |
100 |
2.00 |
1.00 |
| S |
P7 |
S.82 CBPR researchers apply problem-solving abilities in this process |
100 |
1.50 |
1.00 |
| AC |
P7 |
S.83 CBPR researchers continue to assess and reevaluate throughout the project rather than wait until the end of the research phase |
100 |
1.00 |
1.00 |
| K |
P8 |
S.84 CBPR encourages researchers to consider how to apply the knowledge acquired through their collaborations to directly benefit the community being studied |
100 |
1.00 |
1.00 |
| K |
P8 |
S.85 An important element of CPBR is inviting community partners in the dissemination of research findings |
100 |
1.00 |
1.00 |
| A |
P8 |
S.86 CBPR researchers recognize the importance of including community partners in sharing the results with the community |
100 |
1.00 |
1.00 |
| A |
P8 |
S.87 CPBR researchers recognize the importance of finding innovative ways in partnering with community partners in disseminating research results |
100 |
1.00 |
1.00 |
| A |
P8 |
S.88 CBPR researchers understand the importance of having research results readily available |
92.9 |
2.00 |
1.00 |
| S |
P8 |
S.89 CBPR researchers have the ability to communicate findings in a way that could be understood by the community (e.g. being concise, clear, and using appropriate language) |
100 |
1.00 |
1.00 |
| AC |
P8 |
S.90 CBPR researchers share results in community settings such as town hall meetings, presentations at local venues, community newsletters, and brochures |
92.9 |
1.00 |
1.00 |
| K |
P9 |
S.92 Sustainability in CBPR means the community must desire the project to continue |
85.7 |
2.00 |
1.00 |
| K |
P9 |
S.93 CBPR research actions produce preliminary accomplishments, which, in turn, improve community trust and create sustainability |
85.7 |
2.00 |
1.00 |
| A |
P9 |
S.95 CBPR researchers commit to continued relationships and networks within the community beyond a particular project or funding phase |
100 |
1.00 |
1.00 |
| A |
P9 |
S.96 CBPR researchers understand that the community partnership may not end when the project ends |
92.9 |
1.00 |
1.00 |
| S |
P9 |
S.97 CBPR researchers, in collaboration with community partners, have the ability to create a long-term vision |
92.9 |
1.50 |
1.00 |
| AC |
P9 |
S.99 CBPR researchers take the initiative to form and sustain trust through continuous community involvement |
92.3 |
1.00 |
1.00 |
| AC |
P9 |
S.101 CBPR researchers strive for a wide range of outcomes that may include impacting local policy, relational changes, sustainability, cultural awareness, reducing health disparities, and/or improved health outcomes |
92.9 |
1.00 |
1.00 |
|
|
Round 2 (Expert Contributed Statements) |
|
|
|
| K |
P1 |
S.1 The term “CBPR Researchers” applies to both academic and community partners |
85.7 |
1.5 |
1.00 |
| K |
P1 |
S.3 There is no one way to engage in CBPR |
92.9 |
1.00 |
1.00 |
| K |
P1 |
S.4 CBPR researchers need to know about what projects or plans have and have not worked in the past |
85.7 |
1.50 |
1.00 |
| K |
P1 |
S.5 CBPR is a philosophy that guides how a researcher engages a community in a respectful, honoring, and professional way |
100 |
1.00 |
1.00 |
| S |
P1 |
S.7 Researchers must practice cultural competence and be willing to work across different cultures, community identities, and varying needs |
100 |
1.00 |
0.25 |
| K |
P1 |
S.8 CBPR researchers need to know strategies for identifying and engaging relevant community partners |
100 |
1.00 |
1.00 |
| K |
P2 |
S.9 CBPR researchers need to know and/or be willing to learn about the community’s issues, concerns, and strengths |
100 |
1.00 |
0.25 |
| K |
P2 |
S.10 CBPR researchers need a strengths-based concept of skills |
100 |
1.00 |
1.00 |
| S |
P2 |
S.11 CBPR researchers should make skillful use of technology |
100 |
2.00 |
0.50 |
| A |
P2 |
S.12 CBPR researchers need to recognize that communities have strengths, assets, intelligence, history, wisdom, and perspectives that matter |
100 |
1.00 |
0.00 |
| A |
P2 |
S.13 CBPR researchers should be open-minded, better at listening than talking, and should know how to link project partners in meaningful ways |
100 |
1.00 |
0.25 |
| K |
P3 |
S.14 CBPR researchers need to be aware of personal biases |
100 |
1.00 |
0.25 |
| K |
P3 |
S.15 CBPR researchers need to know how to build trust and rapport with partners |
100 |
1.00 |
0.00 |
| S |
P3 |
S.16 Carrying out CBPR requires researchers to pay attention to power differentials that may emerge in the work |
100 |
1.00 |
0.00 |
| S |
P3 |
S.19 Carrying out CBPR requires researchers to be effective and reflective listeners |
100 |
1.00 |
0.25 |
| S |
P3 |
S.20 Carrying out CBPR requires researchers to have group facilitation skills |
100 |
1.00 |
1.00 |
| S |
P3 |
S.21 Researchers will demonstrate strong partnership skills (negotiating, collaborating, networking, liaising) |
100 |
1.50 |
1.00 |
| A |
P3 |
S.23 Carrying out CBPR projects requires researchers to be non-judgmental |
92.9 |
2.00 |
1.00 |
| A |
P3 |
S.24 CBPR researchers need to be willing to share power and control |
100 |
1.00 |
1.00 |
| A |
P3 |
S.25 CBPR researchers need to be honest and able to navigate academic and community settings with ease and transparency |
92.9 |
1.00 |
1.00 |
| A |
P3 |
S.26 CBPR researchers should value egalitarianism |
92.9 |
1.00 |
1.00 |
| A |
P3 |
S.27 CBPR researchers should be cognizant of systems of oppression and privilege |
100 |
1.00 |
0.25 |
| AC |
P3 |
S.29 CBPR researchers need to experience shared decision-making in their projects |
100 |
1.00 |
1.00 |
| K |
P4 |
S.30 Researchers must be knowledgeable about the principles of CBPR in order to decide with the partner community which of those principles will guide their work together |
85.7 |
2.00 |
1.00 |
| K |
P4 |
S.31 CBPR researchers need the ability to collaborate with community stakeholders by trusting them as experts in the research process |
92.9 |
1.00 |
1.00 |
| K |
P4 |
S.32 CBPR researchers need to understand that CBPR is about relationships and relationship building |
100 |
1.00 |
1.00 |
| K |
P4 |
S.33 CBPR researchers must learn about current community processes |
92.9 |
1.50 |
1.00 |
| S |
P4 |
S.34 Carrying out CBPR requires flexibility |
100 |
1.00 |
0.00 |
| S |
P4 |
S.36 CBPR projects require strong communication skills (including clarity, openness, deep listening, curiosity, cultural humility) |
100 |
1.00 |
0.25 |
| A |
P4 |
S.38 CBPR researchers must recognize what they do not know or that they may not be the most knowledgeable about the community within which they work, rather than insisting on their own expertise |
100 |
1.00 |
1.00 |
| A |
P4 |
S.39 CBPR requires valuing co-learning |
92.9 |
1.00 |
0.25 |
| A |
P4 |
S.40 CBPR requires that we leave our academic egos at the door and allow the community to fully “own” the project |
92.3 |
1.00 |
1.00 |
| AC |
P4 |
S.41 Researchers need to spend time listening to the community in which they work in order to build trust and rapport |
100 |
1.00 |
0.00 |
| AC |
P4 |
S.42 Researchers should practice deep listening as a means of gathering qualitative data from engagement activities |
100 |
1.00 |
0.00 |
| AC |
P4 |
S.43 Carrying out CBPR requires interactive community involvement |
100 |
1.00 |
0.00 |
| AC |
P4 |
S.44 Carrying out CBPR requires a willingness to be educated about community by community members |
100 |
1.00 |
1.00 |
| K |
P5 |
S.45 CBPR researchers need knowledge about participatory research |
100 |
1.00 |
1.00 |
| K |
P5 |
S.46 CBPR researchers need to know how to conduct qualitative and quantitative or mixed methods research designs |
100 |
2.00 |
1.00 |
| K |
P5 |
S.48 CBPR researchers need to know or learn how to do culturally responsive research |
100 |
1.00 |
0.25 |
| A |
P5 |
S.50 Researchers should be able to balance providing structure with knowing when to let go and let the group process prevail |
100 |
1.00 |
1.00 |
| S |
P5 |
S.52 Carrying out CBPR requires flexibility |
100 |
1.00 |
1.00 |
| S |
P5 |
S.53 CBPR projects require strong communication skills (including clarity, openness, deep listening, curiosity, cultural humility) |
100 |
1.00 |
0.25 |
| A |
P5 |
S.55 CBPR researchers must recognize what they do not know or that they may not be the most knowledgeable about the community within which they work, rather than insisting on their own expertise |
100 |
1.00 |
1.00 |
| K |
P6 |
S.62 CBPR researchers should be aware of the strengths and barriers of the community |
91.7 |
1.00 |
1.00 |
| K |
P6 |
S.63 The notion of “effective” in CBPR research is community-specific |
85.7 |
1.00 |
1.00 |
| K |
P6 |
S.65 CBPR researchers need cultural competency with respect to the community in which the research is conducted |
100 |
1.00 |
1.00 |
| S |
P6 |
S.67 Community partners should be advocates for change |
85.7 |
1.00 |
1.00 |
| A |
P6 |
S.68 Researchers need to be committed to an ecological approach |
85.7 |
1.00 |
1.00 |
| K |
P6 |
S.70 CBPR researchers need to know or learn how to do culturally responsive research |
100 |
1.00 |
0.00 |
| K |
P7 |
S.72 CBPR researchers need knowledge of the parameters of CBPR |
85.7 |
1.50 |
1.00 |
| K |
P7 |
S.73 CBPR researchers should know how to conduct nonlinear, cyclical research studies that inform policies, strengthen communities, and reduce disparities |
92.9 |
1.00 |
1.00 |
| A |
P7 |
S.74 CBPR researchers understand that process matters |
100 |
1.00 |
1.00 |
| A |
P7 |
S.75 CBPR researchers must be flexible and adaptable |
78.6 |
1.00 |
0.25 |
| AC |
P7 |
S.76 CBPR researchers need to be flexible and persistently observing |
100 |
1.00 |
0.25 |
| S |
P8 |
S.77 Successful CBPR projects will involve researchers who can communicate in lay language that a wide range of stakeholders will understand |
100 |
1.00 |
1.00 |
| S |
P8 |
S.78 CBPR researchers need to be able to translate scientific and research writing into plain language, and multiple languages if necessary |
100 |
1.00 |
1.00 |
| K |
P9 |
S.79 CBPR researchers need knowledge about how to broker the administrative aspects of CBPR (e.g., community subcontracts) |
100 |
2.00 |
1.00 |
| K |
P9 |
S.80 CBPR researchers need knowledge about academic institutional barriers to CBPR and how to overcome them |
100 |
2.00 |
1.00 |
| K |
P9 |
S.83 CBPR researchers need to know about the specifics of the CBPR process (e.g., how to form an advisory board) prior to beginning |
85.7 |
1.50 |
1.00 |
| S |
P9 |
S.86 Researchers need to be skilled in project management |
78.6 |
2.00 |
0.25 |
| AC |
P9 |
S.88 CBPR researchers need to spend in-depth time getting to know the community |
100 |
1.50 |
1.00 |
| AC |
P9 |
S.90 Carrying out CBPR projects requires organizing and planning meetings, data collection, data analysis, and training of others in research methods |
92.9 |
1.00 |
1.00 |
| AC |
P9 |
S.91 Carrying out CBPR projects requires frequent meetings and other forms of communications with partners |
92.9 |
1.50 |
1.00 |
| A |
CT |
S.93 Carrying out CBPR requires a researcher to have a positive outlook about the project |
78.6 |
2.00 |
0.50 |
| A |
CT |
S.96 Carrying out CBPR requires researchers to be flexible |
100 |
1.00 |
1.00 |
| A |
CT |
S.97 Carrying out CBPR requires researchers to be persistent |
92.9 |
2.00 |
1.00 |
| A |
CT |
S.98 Carrying out CBPR requires researchers to be patient |
92.9 |
1.00 |
1.00 |
| A |
CT |
S.100 Self-reflection is central to CBPR |
85.7 |
1.50 |
1.00 |
| A |
CT |
S.101 Humility is central to CBPR |
92.9 |
1.50 |
1.00 |
| A |
CT |
S.103 Beneficence is central to CBPR |
100 |
1.50 |
1.00 |
| K |
M |
S.104 Researchers need to acquire knowledge about how to frame CBPR work in their promotion, tenure materials, and IRB submissions |
92.9 |
1.00 |
1.00 |
| K |
M |
S.105 Researchers need knowledge about the availability of resources to support CBPR |
92.9 |
2.00 |
1.00 |
| K |
M |
S.106 Researchers would benefit from training or workshops in CBPR process |
100 |
1.00 |
1.00 |
Note. Final list of CBPR competencies only includes statements that met criteria for present study: (1) the statement had 70% of experts agree (responding ‘Agree’ or ‘Strongly Agree’); (2) the statement scored a 2.5 or less for the median; and, (3) the statement achieved an IQR of less than or equal to 1. Domain/Categories include: K = Knowledge, S = Skills, A = Attitudes, AC = Activities. Subcategories include: P1 = CBPR Principle 1; P2 = CBPR Principle 2; P3 = CBPR Principle 3; P4 = CBPR Principle 4; P5 = CBPR Principle 5; P6 = CBPR Principle 6; P7 = CBPR Principle 7; P8 = CBPR Principle 8; P9 = CBPR Principle 9; CT = Core Trait; M = Mentoring; S = Statement; Md = Median; % = Percentage; IQR = Interquartile Range.
Tahani Dari, NCC, is an assistant professor at the University of Toledo. John M. Laux is a professor and associate dean at the University of Toledo. Yanhong Liu is an assistant professor at Syracuse University. Jennifer Reynolds is an associate professor at the University of Toledo. Correspondence can be addressed to Tahani Dari, Mail Stop 119, Toledo, OH 43606, Tahani.Dari@rockets.utoledo.edu.
