Abstract
The current study examined associations between effectiveness supporting youth, connectedness with colleagues, and work-related well-being among after-school providers. We used a mixed method approach to gain an in-depth understanding of how effectiveness and connectedness may contribute to provider well-being. Participating after-school providers (n=34) completed a survey examining different aspects of effectiveness supporting youth (i.e., comfort promoting youth social-emotional outcomes, closeness or conflict with youth, ability to engage youth in programming), connectedness with colleagues (i.e., social support and social capital), and work-related well-being (i.e., work engagement, satisfaction, burnout, and stress). A subset of staff (n=11) also completed a follow-up interview to explore their experience of and self-identified facilitators and barriers to their effectiveness supporting youth, connectedness with colleagues, and work-related well-being. We used quantification of qualitative data and integrated data tables to consolidate qualitative and quantitative findings. Effectiveness supporting youth served as the most consistent predictor of work-related well-being across qualitative and quantitative data, including the capacity to build close and positive relationships with youth, engage youth in programming, and promote youth social emotional development. However, some providers also reported emotional fatigue from supporting youth experiencing stressful events, particularly in the absence of adequate resources and support from colleagues. Connectedness across colleagues was identified as a stressor when inadequate or ineffective support or communication was noted, but also as a buffer against stress for providers reporting sufficient and effective support and communication. Effective communication, bidirectional support relationships, and bonding social capital (e.g., trust and shared goals) were the most salient aspects of connectedness associated with positive work-related well-being. The current study illustrates pathways in which effectiveness and connectedness can promote or hinder work-related well-being among after-school providers, particularly the importance of building positive and effective relationships across staff and with youth while creating supportive work environments that include access to necessary resources for addressing youth needs.
Keywords: after-school, stress, burnout, engagement, job satisfaction, adult-youth relationships, social capital
Introduction
Over the last decade, enrollment in after-school programs has increased steadily, with demand particularly high among low-income households and historically marginalized communities (Afterschool Alliance, 2014). After-school programs have demonstrated capacity to improve academic and health outcomes for youth (Wadsworth et al., 2018). However, benefits for youth vary (Durlak et al., 2010), requiring effective programming (Vandell et al., 2013). Low work-related well-being, marked by low work engagement and high stress and burnout, is a salient concern among after-school providers and programs with frequent high job demands and limited resources (Affrunti et al., 2018). Of particular concern, low work-related well-being can compromise after-school providers’ ability to facilitate effective programming, a trend identified across youth-serving professions (Glisson et al., 2006).
Therefore, positive work-related well-being among staff is an important contributor to effective after-school programming. At the same time, prior research across after-school (Affrunti et al., 2018; Hwang et al., 2020) and school-based settings (Shernoff et al., 2011) highlight the importance of effectiveness meeting job demands when addressing stress and burnout among youth-serving adults. In addition to effectiveness meeting job demands, connectedness across staff is a frequent predictor of work-related well-being in school and after-school settings (Affrunti et al., 2018; Hwang et al., 2020; Shernoff et al., 2011). Connectedness is a multi-faceted construct and includes positive, trusted, and consistent relationships (Saeri et al., 2018), effective communication (Grawitch et al., 2006), and pathways for obtaining the support needed to be effective in their job (Bakker et al., 2004; Gray-Stanley & Muramatsu, 2011). Connectedness has marked effects on health and well-being (Saeri et al., 2018), and is critical for workplace longevity and effectiveness for adults, particularly in emotionally demanding jobs (Green et al., 2013). Conversely, poor or unpredictable relationships and social interactions can detract from adults’ job performance (Janssen & Giebels, 2013) and detract from experiences of job satisfaction (Hwang et al., 2020). The current study examines connectedness with colleagues and effectiveness supporting youth as two constructs associated with work-related well-being among after-school providers in a multi-site, middle-school age program serving predominantly diverse and low-income communities.
The Unique Potential of After-School Programs
After-school programs can promote positive short- and long-term health and academic trajectories for youth (Ettekal et al., 2015) by (1) promoting skills (e.g., problem solving, emotion regulation, effective communication) critical to resilience and common to prevention programs (Boustani et al., 2015); and (2) facilitating coping with peers and trusted adults while increasing community and cultural identity (Wadsworth et al., 2018). However, program benefits vary (Durlak et al., 2010). Literature has converged on a set of characteristics common across high quality out-of-school time programs (Vandell et al., 2013): (1) positive staff-youth relationships; (2) positive youth-youth relationships; (3) academic and social-emotional skill-building opportunities combined with a mastery orientation; (4) high youth engagement; and (5) a combination of structure and opportunities for youth autonomy and choice.
Hence, central to high quality after-school programs are opportunities for strong relationships with supportive nonparental adults (Huang et al., 2007), with supportive adult-youth relationships long associated with positive youth outcomes (McLearn et al., 1998). This presents a unique benefit of after-school settings, which invite youth and adults to interact in more relaxed settings through engaging activities that can help to build positive and trusted adult-youth relationships (Grossman & Bulle, 2006). In addition to building positive adult-youth relationships, after-school programs frequently provide opportunities to develop key social emotional learning (SEL) skill that can promote short- and long-term youth well-being and academic success (Frazier et al., 2019; Minney et al., 2019; Moses et al., 2025). However, youth engagement in programming is also necessary for youth to benefit from SEL skill building opportunities (Fisher et al., 2023). Therefore, the ability to build positive relationships with youth, facilitate SEL skill building opportunities, and engage youth in programming are important characteristics of effective after-school providers. Low work-related well-being is one factor that can influence providers’ ability to effectively meet these demands.
Work-Related Well-Being is Important for Youth and Adult Well-Being and Job Effectiveness
Work-related well-being is a multidimensional concept that has been defined and measured in multiple ways (Chari et al., 2018). Within broader definitions of well-being are more specific constructs, such as work-related affective well-being. Work-related affective well-being includes individuals’ emotional experiences, reactions, and health within the workplace. It is measured along multiple dimensions, including pleasure-displeasure (e.g., job satisfaction), anxiety-comfort (e.g., occupational stress), fatigue-vigor (e.g., burnout), and enthusiasm-depression (e.g., engagement) (Rothmann, 2008). These dimensions represent both the absence of well-being, including emotional strain (i.e., occupational stress, burnout, compassion fatigue), and the presence of well-being, including enjoyment and engagement in the work (i.e., job satisfaction, engagement, compassion satisfaction). This study focuses on both the positive and negative dimensions of work-related affective well-being, which we refer to as work-related well-being moving forward for ease of reading.
Work-related well-being is particularly important for youth-serving providers, or adults who support youth in schools and other community settings or programs such as after-school. There has been limited examination of work-related well-being among after-school providers (e.g., Affrunti et al., 2018; Hwang et al., 2020). However, important trends can be gleaned from other youth-serving and educational settings, such as schools. Educators, a particularly salient youth-serving provider, consistently report higher levels of psychological stress, burnout, and distress compared to other professions (Guglielmi & Tatrow, 1998; Kovess-Masfétyet al., 2007). High levels of emotional strain (i.e., stress, burnout, compassion fatigue) and low levels of engagement and satisfaction can negatively impact the wellbeing of youth-serving adults, with high emotional strain associated with poor physical and mental health outcomes and compromised relationships with coworkers and loved ones (Gonçalves et al., 2019; Shernoff et al., 2011).
In addition to impacting their own well-being, high levels of emotional strain can detract from youth-serving providers’ effectiveness supporting and engaging youth. This can be seen in previous research identifying negative associations between stress and job performance among educators (Collie et al., 2012), including educators’ capacity to develop positive relationships with youth. For example, primary school teachers (n = 465) reporting lower burnout have reported more tolerance of disruptive and oppositional behaviors (Kokkinos et al., 2005). Conversely, middle school teachers (n = 58) endorsing higher burnout have been observed to engage in lower levels of emotionally supportive interactions with students (Braun et al., 2019). High emotional strain can also decrease the likelihood of successful adoption and implementation of new skills and practices (Donat & McKeegan, 1997), mitigating youth-serving adults’ capacity to increase their effectiveness over time. High burnout also contributes to increased turnover (Imran et al. 2017), and, in turn, organizational and relational instability, decreasing organizational-level service quality (Glisson et al., 2006). High emotional strain and low job engagement can therefore have detrimental effects on youth-serving adults’ capacity to effectively engage and support youth.
Effectiveness and Connectedness Predict Work-Related Well-Being
While low work-related well-being can have negative impacts on the effectiveness of youth-serving adults (e.g., after-school staff) these relationships are typically bidirectional, with low effectiveness also increasing risk of low well-being. These trends are captured in prevalent work-related well-being theories, such as the Job Demands-Resources (JD-R) Model (Demerouti et al., 2001). The JD-R Model frames excessive job demands as the primary driver of emotional strain, including burnout and stress, with job demands defined as, “those physical, social, or organizational aspects of the job that require sustained physical or mental effort, and are therefore associated with certain physiological and psychological costs” (p. 501). Alternatively, job resources drive engagement and motivation, and are defined as, “those physical, social, or organizational aspects of the job that may do any of the following: (a) be functional in achieving work goals; (b) reduce job demands and the associated physiological and psychological costs; and (c) stimulate personal growth and development” (p. 501). Therefore, the capacity to meet job demands (i.e., effectiveness) predicts emotional strain, while resources predict work engagement.
Job resources can be tangible (e.g., financial resources) or intangible (e.g., knowledge, relationships), and include valued material, personal, energetic, or social resources (Hobfoll, 2001). Resources can exist within an individual (e.g., skills, training, personality) or within social systems. System-level resources include: 1. Social processes (i.e., social norms and interactions); 2. Resources (i.e., economic, physical, human, social, and temporal); and 3. The allocation and arrangement of resources across settings and systems (Seidman, 2012). Systems-level approaches to work-related well-being emphasize a movement away from individualistic conceptualizations of well-being, making explicit efforts to capture, understand, and promote relational and collective influences (Deneulin & McGregor, 2010). These theories and frameworks therefore highlight connectedness within the workplace as a salient resource, predictor, and facilitator of work-related well-being.
Understanding Work-Related Well-Being Requires an Ecological Approach
The importance of connectedness is highlighted in social-ecological paradigms detailing the collective impacts of intersecting layers of individual and social influences (Bronfenbrenner, 1979). These layers include individual factors (e.g., knowledge and personality), the microsystem (i.e., relationships with others within the same system), mesosystem (i.e., interactions occurring across microsystems), exosystem (i.e., surrounding social systems that may influence behaviors within other systems), and macrosystem (i.e., broader economic, political, and cultural influences impacting microsystems). Each of these socio-ecological levels include a focus on relationships, systems, and intersections between the person and their context.
Each ecological level can also increase our understanding of after-school providers’ work-related well-being and intersections with their job effectiveness and connectedness. The microsystem level reflects interactions between after-school providers and between providers with their supervisors, youth in the program, and youths’ caregivers or parents. Resources at the microsystem level frequently come in the form of social support. Social support from supervisors and coworkers is among the most robust resources for buffering against job stress and burnout (Bakker et al., 2004), particularly under conditions of high job demands (Gray-Stanley & Muramatsu, 2011). Different types of support relate differently to burnout (Morelli et al., 2015). For instance, instrumental support (direct hands-on assistance towards meeting job demands) has a greater impact on strain (i.e., burnout, stress) (Halbesleben, 2006), while emotional support (work-related social support not directly targeting job demands) has been more strongly associated with work engagement.
Interpersonal relationships within and across ecological levels also yield collective resources in the form of bridging and bonding social capital. Bridging social capital includes connections with a wide range of people, enabling increased access to tangible (e.g., materials) and intangible (e.g., information) resources. Bonding social capital reflects close relationships with others that form and maintain group norms and breed trust, both at the dyadic and group level (Putnam, 2001).
Bridging and bonding social capital are often conceptualized and measured in terms of social network connections (Neal & Neal, 2019). From a networks perspective, bridging social capital is typically conceptualized as a larger number of “weak” ties with people who bring different experiences and knowledge, often measured via someone’s advice network size (i.e., number of people they go to for advice) and their level of betweenness, or the extent to which they serve as a gatekeeper of information between other people in the network (Neal & Neal, 2019). Bonding social capital can be conceptualized by the number of close ties (e.g., friendship ties) a person has in their immediate network, as well as the level of connectedness between the surrounding people in someone’s network (i.e., density) (Neal & Neal, 2019), indicating a close-knit social community which can contribute to high levels of trust at the group level (Moolenaar & Sleegers, 2010).
Studies of social capital among teachers, nurses, physicians, and mental health professionals reveal that high social capital relates to high work engagement (Strömgren et al., 2016), low strain (Kowalski et al., 2010), and high job performance (AbuAlRub, 2004). Bonding social capital relates more strongly to job satisfaction (i.e., engagement), while bridging social capital relates more strongly to job performance (i.e., capacity to meet job demands; Huang & Liu, 2017). Social capital can be facilitated within organizations by creating structured and routine opportunities (e.g., team meetings) for staff within that organization to communicate, share ideas, and build cohesion (Baker & Dutton, 2007). This literature illustrates how connectedness across colleagues, including different forms of social support and social capital, can facilitate increased work-related well-being. However, more research is needed that explores these relationships within after-school specifically.
Work-Related Well-Being in After-School Requires Both Effectiveness and Connectedness
While still a burgeoning area of study, after-school research highlights the importance of effectiveness and connectedness in predicting work-related well-being. For instance, Affrunti et al. (2018) found that job demands were the strongest predictor of stress (i.e., emotional strain) for recreational park-based after-school staff (n = 94) in a large Midwestern city. In a mixed method small sample study, Hwang et al. (2020) administered one round of surveys and qualitative interviews/focus groups to after-school staff serving youth grades 3–8 in a community based after-school program (with academic, social-emotional, and recreational components) operating out of predominantly public schools in under-resourced neighborhoods in the northeastern US. Relationships with youth were identified as a critical source of encouragement and motivation (i.e., engagement) for staff. They also highlighted connections with supervisors and colleagues for sharing information, informal and formal mentoring, and moral support, as integral components of their microsystem-level professional support system.
What We Know, What We Don’t Know, and the Current Study
After-school programs can promote positive outcomes for youth (Pierce et al., 2010), but benefits rely on positive and strong adult-youth relationships, skill building opportunities, and youth engagement in programming, which can be compromised by low work-related well-being. There has been limited mixed method examination of the associations between effectiveness and connectedness with work-related well-being in after-school. Research in related settings, such as schools, emphasizes that emotional well-being predicts effectiveness of youth-serving adults to build positive adult-youth relationships; with high stress and burnout associated with fewer positive relationships and increased conflict with youth. Conversely, connectedness with coworkers (i.e., communication, support, and social capital) and effectiveness supporting youth (i.e., ability to build positive relationships, youth engagement, and SEL skill building opportunities) predict positive work-related well-being (i.e., engagement and satisfaction) and negative well-being (i.e., emotional strain including stress and burnout). Specifically, increased connectedness and effectiveness are expected to be associated with higher engagement and satisfaction and lower burnout and stress (Figure 1).
Figure 1.
Conceptual Model with Predicted Associations between Effectiveness Supporting Youth, Staff-to-Staff Connectedness, and Staff Work-Related Well-Being.
In the current study we use quantitative surveys, including social networks, and qualitative interviews to yield a contextualized understanding of effectiveness, connectedness, and work-related well-being for after-school providers serving predominantly racially and culturally diverse middle school age youth. Our three primary research goals are: (1) Understand and describe after-school providers’ experience of effectiveness supporting youth, connectedness between staff, and work-related well-being; (2) Test hypothesized associations between effectiveness (i.e., capacity to build close and positive relationships with youth, engage youth in programming, and promote youth social emotional development) and connectedness (i.e., communication opportunities and social network connections) with work-related well-being; we predict higher effectiveness and connectedness to be associated with positive well-being (i.e., engagement and satisfaction), and lower effectiveness and connectedness to be associated with negative well-being (i.e., emotional strain including burnout and stress); and (3) Identify facilitators of and barriers to work-related well-being by quantifying and integrating the qualitative interview and quantitative survey feedback to sort providers into “low”, “medium”, and “high” work-related well-being groups to examine for differences in participant identified facilitators and barriers.
Method
Partnering After-School Program
The collaborating program will be referred to as After-School Program (ASP) to maintain confidentiality. ASP is a multi-site, not-for-profit after-school program, providing both after-school and summer programming for middle-school students (grades 6–8) in a city located in the Southeastern US. The program provides structured opportunities for promoting youth academic success, health, and wellness, including homework support and academic development (e.g., STEM learning and career exploration), enrichment opportunities for promoting key life skills (e.g., substance use prevention, interpersonal skill development, and mental health promotion), and recreation (e.g., sports and performing arts).
In the year the study was conducted (2019–2020) there were 14 ASP sites total, each operating out of a different local public school. Students are grouped by grade, with one or two frontline providers and 10 to 30 students per group. Each site serves 40 to 100 students and employs six to 13 frontline providers. All ASP site managers and after-school providers are certified teachers, and most teach during the day at the same school as their ASP site. In the year of the study, 52% of enrolled youth identified as Black/African American, 45% as Latino/a, 2% White and 1% Other, with 85.4% of students qualifying for free or discounted lunch.
Partnership Summary
The community-academic partnership began in September 2019. Following initial meetings between ASP’s executive director, two program directors, and researchers (first and last authors), leadership invited support for staff towards: promoting social emotional learning and student mental health, supporting relationships between staff and students, and promoting staff well-being. The first author began weekly site visits in November 2019 at four ASP sites, selected by leadership to illustrate variation related to youth populations, staff strengths, and program needs. All four sites serve predominantly Hispanic and/or Black communities, with two located in neighborhoods with high rates of poverty (over 20% of residents below the poverty line). ASP selected the four sites to reflect variation in student populations and site-level readiness across their program. Site visits were organized to observe programming; hear directly from staff about their role, job demands, strengths, and needs; and to initiate collaboration with site-level staff, managers and leaders. Site visits continued until ASP’s shift to remote programming in March 2020 due to COVID-19.
Procedure
Data collection recruitment began in May 2020, after ASP had adjusted to remote programming. All recruitment and data collection materials and procedures were approved by ASP leadership and the university Institutional Review Board. Site managers across all 14 sites received information about the study and were encouraged (but not required) to share it with their staff. Recruitment occurred in two phases based on ASP preferences. First, in May 2020, with permission from ASP leadership, site managers at the four collaborating sites received emails and were encouraged to share the opportunity with their staff. All staff, including frontline providers, site managers, and support staff were invited to participate. Second, in December 2020, program leadership sent a brief description of the study and link to the survey to their additional 10 site managers. Online consent was collected before participants could begin the survey.
Data collection included an online survey and semi-structured interview. The survey was conducted in Qualtrics and divided into two parts to prioritize completion of at least one measure of each construct (i.e., effectiveness, connectedness, well-being). Staff received a $30 gift card for completing part one (30–45 minutes) and an additional $10 for completing part two (~15 minutes). Before each individual measure, participants were prompted to think specifically about their in-person after-school experience prior to COVID-19 (to maximize application of findings to traditional programming).
All participants were invited to participate in a follow-up one-hour individual interview (range 40–79 minutes, M = 55.20 minutes, SD = 11.72). Interviews were facilitated by the lead author over the phone or Zoom, at a time of each participant’s convenience, following verbal and online consent. Interviews were conducted between August 2020 and February 2021 and audio recorded and transcribed via NVivo Transcription software. All identifiable information, except for participant ID numbers, was removed from transcripts. One pair of providers were interviewed together by their request. Interview participants received a $40 gift card.
Participants
Overall, a total of 34 staff completed the survey (~30% of eligible staff), across eight sites (57% of eligible sites), with 27 participants completing all surveys for the full $40 and seven completing only the first portion for $30. Participation included 19 surveys (8 follow-up interviews; ~59% of eligible staff) from the four collaborating sites (that had been visited by the lead author prior to COVID-19), and 15 surveys (3 interviews; ~47% of eligible staff) from four non-collaborating sites (not selected by leadership for site visits). ANOVAs revealed no statistically significant differences (p > .05 across analyses) in primary outcomes between collaborating and non-collaborating sites; therefore, data were aggregated and examined together. Table 1 includes neighborhood demographics for the eight sites, based on United States Census data, to reflect the potential demographic served. It is important to note that this is a proxy measure of youth demographics and may not reflect the exact population served by that program. Table 2 presents demographic information for survey participants (n=34) and the subsample who completed a follow-up interview (n=11). Out of the survey participants, the number of participants per site ranged from one to seven, with a mode of four participants per site.
Table 1.
Neighborhood-Level Characteristics and Youth Demographics.
| Characteristics* | Site 1 | Site 2 | Site 3 | Site 4 | Site 5 | Site 6 | Site 7 | Site 8 |
|---|---|---|---|---|---|---|---|---|
| Black/African American | 82.6 | 2 | 41.4 | 27.7 | 2.4 | 7.3 | 58.7 | 85.2 |
| White | 14.5 | 93.4 | 46.8 | 64.8 | 90.6 | 83.8 | 34.4 | 10.4 |
| Two or more races | 0.9 | 2.2 | 10.1 | 4 | 2.2 | 2.9 | 2.4 | 1.9 |
| Hispanic | 13.7 | 83.8 | 59.3 | 63.5 | 91.0 | 76.0 | 41.0 | 14.8 |
| % below poverty line | 18.6 | 11.7 | 36.4 | 25.7 | 18.5 | 10.3 | 54.3 | 22.7 |
Note: All values are percentages are based on census metrics from the neighborhood in which the after-school site was located and may not reflect the exact demographics of youth participating in the after-school program at that site.
Table 2.
Participant Demographics.
| Quantitative Sample (n=34) | Qualitative Sub-Sample (n=11) | |||||
|---|---|---|---|---|---|---|
| Characteristic | Nominal variables | Continuous variables | Nominal variables | Continuous variables | ||
| % | Mean | SD | % | Mean | SD | |
| Job Role | ||||||
| Frontline Provider | 58.8 | 81.8 | ||||
| Site Manager | 26.4 | 9.1 | ||||
| Administrative Support | 8.8 | 9.1 | ||||
| Security | 2.9 | 0 | ||||
| Counselor | 2.9 | 0 | ||||
| Gender | ||||||
| Male | 20.6 | 18.2 | ||||
| Female | 73.5 | 81.8 | ||||
| Other or Not Reported | 5.9 | 0 | ||||
| Race | ||||||
| Black/African American | 41.2 | 27.3 | ||||
| Black/Caribbean American | 14.7 | 27.3 | ||||
| Middle Eastern/Arab American | 2.9 | 9.1 | ||||
| White | 29.4 | 27.3 | ||||
| Not Reported | 14.7 | 18.2 | ||||
| Ethnicity | ||||||
| Hispanic | 23.5 | 9.1 | ||||
| Non-Hispanic | 67.6 | 81.8 | ||||
| Not reported | 8.8 | 9.1 | ||||
| Highest degree | ||||||
| B.A./B.S. or under | 50.0 | 36.4 | ||||
| M.A./M.S. | 44.1 | 54.5 | ||||
| Ph.D/Ed.D | 5.9 | 9.1 | ||||
| Age | 46.7 | 10.7 | 42.5 | 10.7 | ||
| Number of years with after-school program | 6.31 | 5.0 | 5.8 | 5.4 | ||
| Number of years working with children (prior) | 4.41 | 1.5 | 4.55 | 1.2 | ||
Mixed Method Approach
Researchers have highlighted the particular benefit of mixed methods for examining complex and multifaceted phenomena (Castro et al., 2010), such as well-being (Jones & Sumner, 2009), and for collaborating with populations who have been systematically excluded or misrepresented in research (Ponterotto et al., 2013). Quantitative approaches allow the opportunity to operationalize, measure, compare, and examine associations between different concepts and phenomena, however these methods also risk becoming decontextualized and overgeneralized (Moghaddam et al., 2003; Viruel-Fuented, 2007). Qualitative approaches conversely offer a more contextualized approach (Gelo et al., 2008), while allowing people to share their experiences of different phenomena in their own words within their particular context (Guba & Lincoln, 1994). By applying quantitative and qualitative approaches to understanding the same phenomena (i.e., well-being, effectiveness, connectedness), we can gain a more comprehensive and contextualized understanding of each construct, how they are interrelated, and opportunities for promoting positive outcomes. Comparing results across methods can also highlight potential limits to the conceptual model and quantitative measures, informing the cultural relevance or potential limitations of selected constructs and measures.
The research takes a pragmatic approach (Tashakkori & Teddlie, 1998), common to mixed-method approaches (Johnson & Onwuegbuzie, 2004), facilitating integration of information based on identified research questions and goals. Surveys and interviews correspond directly to the conceptual model, and were administered simultaneously (i.e., some participants were interviewed as others were completing the survey, QUAL + QUANT; Hanson et al., 2005). We used a convergent parallel, or triangulation, design (Creswell et al., 2003; Creswell & Plano Clark, 2017), where quantitative and qualitative data were collected and analyzed separately. Results were merged to compare, validate, and extrapolate findings across data strands, with equal emphasis on both (Johnson & Onwuegbuzie, 2004).
Quantitative Measures
Effectiveness Supporting Youth
Student Teacher Relationship Scale (STRS-SF; Pianta, 2001).
This 15-item measure (5-point scale; 1 = Definitely does not apply to 5 = Definitely applies) examines teachers’ relationships with individual students or with their class as a whole (e.g., Whitaker et al., 2015). We revised “student(s)” to “children” for after-school. The scale is split into two subscales focused on perceived closeness (8 items) and conflict (7 items) with youth. Internal reliability for subscales in the current sample was between acceptable and good (closeness α = .83; conflict α = .74).
Social and Emotional Learning (SEL) Scale for Teachers (Brackett et al., 2012).
This 12-item measure (5-point-scale; 1 = Strongly Disagree to 5 = Strongly Agree) examines educator perceptions of important factors influencing their implementation of SEL programming, organized under three factors (Comfort, Commitment, and Culture). The measure was adapted for after-school by changing language from “school” to “site” or “after-school” and from “principal” to “supervisor”. Only the SEL Comfort domain (4 items) was used in the current study due to its focus on providers’ self-efficacy and comfort promoting SEL with the youth at their site. Internal reliability for SEL Comfort in the current sample was good (α = .84).
Staff-to-Staff Connectedness
Comprehensive Evaluation of Social Support (CESS; Boyar et al., 2014).
The supervisor and coworker subscales of this measure include 12 items (4-point scale; 1 = Strongly Disagree to 4 = Strongly Agree) and measure perceived emotional and instrumental support received from coworkers (three items each for emotional support and instrumental support) and supervisors (three items each for emotional support and instrumental support). Boyar et al. (2014) define emotional support as, “being empathetic, expressing care and concern, and listening,” and instrumental support as, “providing tangible and objective assistance” (p. 2). Internal reliability in the current sample was between good and excellent across all subscales and total scores (supervisor emotional α = .86; supervisor instrumental α = .94; coworker emotional α = .86; coworker instrumental α = .93; coworker and supervisor support total α = .90).
Social Networks.
We collected social network data using an open-ended question format due to providers leaving and joining the program at different times throughout the study. Across each question stem, providers were prompted to list the name, site, and role of each person in ASP they go to for advice/support. We included questions assessing four different advice/support networks, including: 1) advice supporting youth (i.e., “Who do you go to for advice or with questions around supporting youth in your program?”); 2) advice supporting youth mental health and social emotional learning (i.e., “Who do you go to with questions or concerns related to student mental health and promoting social emotional learning?”); 3) emotional support (i.e., “Who do you go to at your site or in your organization for support after a long day or stressful event at work?”); 4) and friendship networks (i.e., “Who from ASP do you socialize with outside of after-school/work hours?”). However, we only included advice networks in the final analysis due to limited variability and range in network size for support of youth mental health and social emotional learning (M=1.86; SD=1.94), emotional support (M=0.67; SD=0.87), and friendship (M=1.35; SD=1.49) networks. For advice networks, we calculated network size (i.e., sum of all people they noted in their network), incoming ties (i.e., number of people who come to them for advice), outgoing ties (i.e., number of people they go to for advice), ego density (i.e., the total number of ties in the participant’s ego network, not counting ties with the participant, divided by the number of pairs, times 100), and ego betweenness (i.e., the number of times a node is on the shortest path between all pairs of nodes in its ego network) for each participant. Social network metrics were calculated using UCINET, a statistical package for analyzing social network data.
Previous research has demonstrated social networks as a measure of both bridging and bonding social capital (Neal & Neal, 2019). Based on this research, we used advice network density as one metric of bonding social capital (i.e., level of closure among social network ties reflected in the extent to which the coworkers a provider goes to for advice also go to each other), and betweenness as one metric of bridging social capital (i.e., the extent to which the provider serves as a bridge or broker between two people who are otherwise disconnected from each other in the network).
Communication Opportunities.
We developed a set of questions assessing where (i.e., physical spaces and technological platforms) and how frequently (e.g., daily, multiple times per week, weekly, monthly, every few months) providers typically communicate with coworkers. Questions included both structured (e.g., scheduled meetings) and unstructured (e.g., unplanned conversations) opportunities to connect via both virtual (e.g., email, text, apps) and in-person (e.g., in-person conversations during the workday, group meetings, individual check-ins with supervisor) formats. For each communication opportunity/format, providers indicated frequency of use, on an eight-point scale from “daily” to “less than one time per year”. The final set of questions assessed for 14 different methods/opportunities to communicate with colleagues. In analyses we use the sum of frequency scores across all communication methods/opportunities. Internal reliability in the current sample was acceptable (α = .66).
Work-Related Well-Being
Of note, the work-related well-being measures used in this study have been used previously with educators (e.g., McMakin et al., 2022; Timms et al., 2011) but have not been used previously in after-school settings. We selected measures based on their focus on both positive well-being (i.e., engagement and satisfaction) and negative well-being (i.e., emotional strain including stress and burnout).
Utrecht Work Engagement Scale (UWES-9; Schaufeli et al., 2006).
This 9-item measure (7-point scale; 0 = Never to 6 = Always) focuses on positive work-related well-being and assesses vigor (three items), dedication (three items), absorption (three items), and overall engagement. We used total engagement, calculated as an average across all nine items, due to a cross-national study which identified greater internal consistency (between .85 to .92) for total engagement compared to individual subscales (Schaufeli et al., 2006). Internal reliability within the current sample was good (α = .83).
Oldenburg Burnout Inventory (OBI; Bakker et al., 2004; Halbesleben & Demerouti, 2005).
The English version of this 16-item measure (4-point scale; 1 = strongly disagree to 4 = strongly disagree) is informed by the job demand-resources model and uses both positively and negatively worded items to assess the two primary components of burnout, exhaustion (eight items) and disengagement (eight items; Demerouti et al., 2001; Demerouti et al., 2010). Internal reliability was between acceptable and good (disengagement α = .60; exhaustion α = .84).
Professional Quality of Life Measure (ProQOL 5; Stamm, 2005; Stamm, 2010).
This 30-item measure (5-point scale; 1 =Never to 5 =Very Often) assesses both positive (compassion satisfaction) and negative (compassion fatigue) aspects of helping professions via three subscales, including compassion satisfaction (10 items) and two aspects of compassion fatigue (i.e., burnout and secondary traumatic stress with 10 items each). Internal reliability was good for compassion satisfaction (α = .88), acceptable for burnout (α = .68), but poor for secondary traumatic stress (α = .52).
Demographics
Participants were also asked for their age (in years), gender (select all that apply: Male, Female, Trans male/Trans man, Trans female/Trans woman, Genderqueer/Gender non-conforming, Self-described, or Prefer not to disclose), ethnicity (Yes/No: Hispanic or Latino/Latina/Latinx), race (select all that apply: Alaska Native, American Indian/Native American, Black/African American, Black/Caribbean American, East Asian/Asian American, South Asian/Indian American, Middle Eastern/Arab American, Native Hawaiian/Pacific Islander, White, Self-describe, Prefer not to disclose), role in ASP (Select one: Site manager, Component Leader, Other [specify]), education (Select one: Less than high school, High school graduate or GED, Some college no degree, Training program degree (e.g. nursing diploma), Associate’s degree, Bachelor’s degree, Master’s degree, Professional degree, Doctoral degree), average number of hours worked per week, years working with ASP, and years working with children prior to joining the program.
Qualitative Semi-Structured Interview Guide
We included broad (e.g., “What parts of your job feel stressful? What parts of your job do you enjoy the most?”), and specific questions (e.g., “What kinds of things do your supervisors or coworkers do that you find helpful when you’re feeling stressed?”) to assess different aspects of each construct (i.e., effectiveness, connectedness, and work-related well-being) and how they may be inter-related. Interviews included questions about both in-person and remote programming to assess how provider experiences evolved during COVID-19. Questions and probes were sequenced and prioritized based on each individual’s survey results (e.g., stress/burnout). Questions were also refined, removed, and added over time based on participant feedback (Galletta, 2013).
Analytic Plan
Quantitative Analyses
Missing Data.
At the individual measure level, no single participant had more than three items missing (19% missing data) for a single measure. Thus, item-level missingness was addressed using mean substitution as done in other studies (e.g., Hazel et al., 2014; Howe et al., 2012). All remaining missing data came from technological malfunctions (e.g., the survey froze; n=2) or participants choosing not to complete the additional surveys (n=7), resulting in 6.8% missing data overall. The first portion of the survey included the STRS-SF, CESS, social network questions, OBI, and UWES. The second portion included the SEL Scale and ProQOL. All logistic regressions predicting missingness were not statistically significant (p >.05), indicating no significant differences in any model variables between participants who did and did not complete the second set of surveys. Therefore, we used multiple imputation, set at 20 imputations, to impute subscale/total scores for missing measures, using demographic data and subscale/total scores for all completed measures as auxiliary variables. We used pooled results from across the 20 imputations for all quantitative analyses.
Quantitative Analytic Approach.
Using pooled results from across the 20 imputations, we conducted bivariate correlations to examine associations between each of the quantitative measures, particularly between measures of effectiveness and connectedness with work-related well-being. We tested assumptions of linearity, normality, and homoscedasticity before conducting the correlations. We did not conduct combined models (e.g., regression models) due to sample size and multicollinearity concerns.
Qualitative Coding and Analysis
Qualitative Approach.
Following transcription, qualitative interviews were coded and analyzed using content analysis (Downe-Wamboldt, 1992). Hsieh and Shannon (2005) identify multiple approaches to content analysis. These approaches include inductive approaches where categories and codes are driven by the data rather than preselected by the researcher. Directed content analysis instead identifies categories deductively (Potter & Levine-Donnerstein, 1999), based on a pre-selected theoretical framework or conceptual model. Researchers have highlighted the potential to mix approaches in a single study, depending on the research question and goal (Kondracki & Wellman, 2002). We used a combination of inductive and deductive codes to gather an in-depth understanding of each construct (i.e., effectiveness, connectedness, and well-being), while examining the potential limits of the proposed conceptual model. For example, we pre-selected specific qualitative categories (e.g., evidence of positive adult-youth relationships), in addition to including broader categories (e.g., parts of the job providers identify being effective at) to assess for aspects of each construct (e.g., effectiveness) that might not be captured by the quantitative measures. This allows for the integration and triangulation of quantitative and qualitative data, while capitalizing on the benefit of qualitative data for capturing a deeper and richer description and experience of each construct.
We relied on Watkin’s (2017) rigorous and accelerated data reduction (RADaR) technique, which includes the use of comprehensive data tables using word processing software, gradually “reducing” data by focusing on pieces of text identified as relevant to each research question. Coding was completed by authors (deidentified for peer review) in a series of steps: (1) Open coding: Lead author and one other coder read every transcript in full, highlighting increasingly more focused pieces of text relevant to the primary research questions. This process resulted in more concise data tables and facilitated progression from open to more “focused codes” (Grinnell & Unrau, 2011). Coders met routinely to inform and refine a final set of codes, organized by construct (i.e., effectiveness, connectedness, well-being); (2) Segmenting and focused coding: The lead author completed the final segmenting and coded each construct (i.e., effectiveness, connectedness, well-being) separately, using methods described by Campbell et al. (2013) and O’Conner and Joffe (2020); and (3) Double coding: One-third of the data (randomly selected) was double-coded independently by a second coder, with discrepancies resolved through consensus discussions (Krippendorff’s alpha of 0.73; Feng, 2014).
Data saturation (Guest et al, 2020) was achieved with nine interviews, with less than 5% new information emerging from the last two interviews. For more nuanced analysis, we present qualitative findings at the code level, letting qualitative themes emerge from overlaps between codes and quantitative results. We report frequencies and illustrative quotes, collectively representing all 11 interviews, for each code in Supplemental Table 1. We use code frequencies to enable the quantification of qualitative results as done in prior studies (Cousins et al., 2024; Nzabonimpa, 2018; Srnka & Koeszegi, 2007; van Grootel et al., 2020), facilitating comparison and integration of quantitative and qualitative data (see “Data Integration” section below). Whenever qualitative data were quantified, results were always recontextualized by returning to the qualitative narratives, including selecting and presenting illustrative quotes (Castro et al., 2010).
Representativeness of the Qualitative Sample.
Approximately one-third of survey participants (n=11) completed interviews. Using the survey data, we calculated percentiles reflecting the top, middle, and bottom third of the sample. At least two (18%) of the qualitative interviewees were in the upper, middle, and lower percentiles for each quantitative construct (i.e., effectiveness, connectedness, well-being), indicating that the eleven qualitative interviewees were representative of the larger quantitative sample.
Author Positionality.
The qualitative coding team reflects multiple racial and cultural identities, and all have training in clinical psychology. The primary author, who selected measures and conducted the interviews, identifies as a White, non-Hispanic, able-bodied, cisgender female from the Northeastern US. Steps to try to mitigate potential biases resulting from this positionality include conducting in-person site visits to facilitate collaborative and bidirectional relationships with ASP staff. Surveys and interview guides were reviewed by ASP leadership for feedback and revisions before data collection. After-school providers were asked about interview questions that felt missing, which were then added to future interviews. Broad (e.g., “What parts of the job do you feel particularly strong or effective at?) and targeted (e.g., “How do you build relationships with the kids at your site?) interview questions were included to check limits to the conceptual model. The interviewer paused often during interviews to reflect and check accuracy of interpretation. Finally, qualitative and quantitative data strands were integrated to check for congruency and discrepancies.
Data Integration
Our convergent mixed method design was informed by Castro et al.’s (2010) integrative mixed methods (IMM) approach and Moseholm and Fetters’ (2017) recommendations for using conceptual models to guide integration of quantitative and qualitative data. Multiple techniques are represented (Fetters et al., 2013): (1) analyzing quantitative and qualitative data separately and comparing findings; (2) analyzing on a “construct-by-construct” basis; (3) data transformation including quantifying qualitative data; and (4) use of joint data displays (Guetterman et al., 2015) to present quantitative and qualitative findings side-by-side, informing inferences across data.
Specifically, for Research Goal 1 (Understand and Describe Effectiveness, Connectedness, and Work-Related Well-Being), we used frequency scale coding to count mentions of each corresponding code/category, using graphs to identify potential outliers and the level of variation in mentions across participants. We then analyzed qualitative and quantitative data separately for Research Goal 2 (Associations Between Effectiveness, Connectedness, and Well-Being), and examined for similarities and/or differences in outcomes. For qualitative data, we coded for self-identified contributors to positive and negative work-related well-being, identifying examples of how connectedness and effectiveness may influence work-related well-being. For Research Goal 3 (Facilitators of and Barriers), using the quantitative measures and qualitative frequencies, providers were sorted into “low”, “medium”, and “high” work-related well-being groups. For individuals with discordant qualitative and quantitative outcomes, qualitative feedback was prioritized due to its closer proximity to providers’ lived experiences. We then examined for differences in qualitatively identified facilitators and barriers between identified groups. Supplemental Table 2 specifies how quantitative and qualitative data strands were integrated to describe and identify facilitators and barriers to staff effectiveness, connectedness, and well-being.
Results
Research Goal 1: Understand and Describe After-School Providers’ Experience of Effectiveness Supporting Youth, Connectedness Between Staff, and Work-Related Well-Being
Means, standard deviations, and ranges for all quantitative measures are reported in Table 3. We first examined quantitative measures of skewness (i.e., asymmetrical/non-normal distribution) and kurtosis (i.e., whether data are heavy- or light-tailed compared to a normal distribution) to identify outliers. Skewness and/or kurtosis concerns were identified for the STRS closeness and conflict scales, UWES (engagement) scale, CESS supervisor and coworker support scales, and ProQOL compassion satisfaction scale. We found low variability across each of these scales, with ceiling effects (i.e., high means and limited variability) across the STRS closeness, UWES, CESS social support, and ProQOL compassion satisfaction scales, and a floor effect (i.e., low means and limited variability) for the STRS conflict scale, as reflected in Table 3. This indicates potential response bias, as the means for all variables were in the “desired” direction, resulting in more limited variation in these variables.
Table 3.
Descriptive Statistics for Measures of Effectiveness, Connectedness, and Well-Being
| Variable | nActual | nImputed | M | SD | Range |
|---|---|---|---|---|---|
| 1. STRS closeness | 34 | 34 | 4.32 | 0.63 | 2.25–5 |
| 2. STRS conflict | 34 | 34 | 1.82 | 0.58 | 1–3.00 |
| 3. SEL comfort | 27 | 34 | 3.94 | 0.59 | 2.75–5 |
| 4. Communication Opportunities | 34 | 34 | 6.44 | 2.36 | 1–10 |
| 5. CESS supervisor emotional support | 34 | 34 | 3.72 | 0.51 | 1.67–4 |
| 6. CESS supervisor instrumental support | 34 | 34 | 3.57 | 0.70 | 1–4 |
| 7. CESS coworker emotional support | 34 | 34 | 3.71 | 0.41 | 3–4 |
| 8. CESS coworker instrumental support | 34 | 34 | 3.73 | 0.46 | 2.33–4 |
| 9. CESS total support | 34 | 34 | 3.68 | 0.41 | 2.67–4 |
| 10. Advice network – total network ties | 24 | 34 | 3.93 | 4.13 | 1–14 |
| 11. Advice networks - density | 24 | 34 | 27.89 | 30.99 | 0–100 |
| 12. Advice networks - betweenness | 24 | 34 | 3.91 | 9.37 | 0–44 |
| 13. OBI - Disengagement (burnout) | 34 | 34 | 1.84 | 0.37 | 1.14–2.43 |
| 14. OBI – Exhaustion (burnout) | 34 | 34 | 1.90 | 0.47 | 1.00–3.14 |
| 15. ProQOL - compassion satisfaction | 25 | 34 | 44.72 | 4.13 | 37–50 |
| 16. ProQOL - burnout | 25 | 34 | 14.32 | 3.16 | 10–21 |
| 17. ProQOL - secondary traumatic stress | 25 | 34 | 16.56 | 3.37 | 11–24 |
| 18. UWES - total | 34 | 34 | 4.80 | 0.78 | 3.11–6 |
Notes: All means, standard deviations, and ranges are for the raw (non-imputed) data. STRS = Student-Teacher Relationship Scale; SEL = Social Emotional Learning; CESS = Comprehensive Evaluation of Social Support; OBI = Oldenburg Burnout Inventory; ProQOL = Professional Quality of Life; UWES = Utrecht Work Engagement Scale
Effectiveness
To evaluate the level of variability in qualitative reports of effectiveness, we tallied each time providers shared evidence of positive and close adult-youth relationships (frequency = 0 to 10, M = 2.82, SD = 3.09 ; e.g., “I found that, for some reason, kids have a tendency to share and disclose information with me.”), evidence of adult-youth conflict (frequency = 0 to 3, M = .73, SD = 1.27 ; e.g., “They don’t want to have to come and sit with me.”), evidence of youth engagement in programming (e.g., “I feel like when kids get to after school, they really enjoy it and their mood just gets better.”), and evidence of youth disengagement (e.g., “They don’t want to do nothing educational. Nothing.”). We calculated a total frequency code for youth engagement (frequency = −6 to 5, M = 1.73, SD = 3.26) by subtracting the number of times a participant indicated low youth engagement from their frequency of indicated positive youth engagement. The frequency counts demonstrate a range of experiences building close adult-youth relationships and engaging youth in programming, but limited reports of adult-youth conflict demonstrating either effective relationships or limited comfort discussing instances of conflict during the interviews.
Connectedness
We tallied each time providers shared evidence of supervisor social support, including instrumental, emotional, or informational support (M = 1.18, SD = 1.83) (e.g., “I definitely think it helps that [the supervisor] is kind of an open person […] she listens to what we have to say. She’ll be like, we have sub teachers. You’re having a stressful week. Just take the next two days.”). We also tallied examples of coworker social support (M = 1.91, SD = 1.81) (e.g., “There would also be times when we would be sharing ideas with each other about different things that we had to do, that would make it less complicated to get going.”); bonding social capital, including the presence of trust and respect as well as alignment of group goals and values (M = 1.45, SD = 1.51) (e.g., “You’re kind of all on the same page, I think that definitely helps.”); and bridging social capital including their work-related connections with people outside their designated after-school site (M = .09, SD = 1.87) (e.g., “What was helpful about those was, you know, the other schools were […] they would tell us how it works in their schools and it would give us some insight as to […] how it was for their kids.”). It is important to note that these frequency counts for connectedness indicate the number of mentions of receiving that type of support, but are not a quantification of the quality of support or how supported the participants felt, which are instead reflected in the quantitative measures.
Work-Related Well-Being
We tallied each time providers shared evidence of engagement in the work (M = 4.00, SD = 2.45) (e.g., “I love the kids. I want to stay right there and interact with them, and I think that’s my thing.”); evidence of disengagement in the work (M = 1.75, SD = .50) (e.g., “The money is good. But, you know, I didn’t realize how tedious it was because it is, it takes a lot.”); evidence of emotional strain (M = 4.91, SD = 4.01) (e.g., “By February, March, [the kids are] exhausting.”); evidence of compassion fatigue (M = 1.60, SD = .894) (e.g., “But at that time I was crying because [a student] was in intensive care. So I was all emotional […]”); and evidence of compassion satisfaction (M = 1.50, SD = .57) (e.g., “She didn’t tell anyone else. She came straight to me and I was like, oh wow. It felt cool.”). Additionally, histograms of qualitative frequencies revealed floor effects across lack of well-being codes, including disengagement, emotional strain, and compassion fatigue, indicating either high levels of well-being among providers and/or variable comfort talking about stressful events during interviews.
Research Goal 2: Examining Effectiveness and Connectedness as Predictors of Well-Being
Quantitative and qualitative results for research goal two are summarized in Table 4.
Table 4.
Integrated Data Table Summarizing Associations Between Effectiveness, Connectedness, and Work-Related Well-Being
| Quantitative Results | Qualitative Results | |
|---|---|---|
| Effectiveness Supporting Youth and Work-Related Well-Being |
|
|
| Connectedness Across Colleagues and Work-Related Well-Being |
|
|
Notes: STRS = Student-Teacher Relationship Scale; SEL = Social Emotional Learning; CESS = Comprehensive Evaluation of Social Support; OBI = Oldenburg Burnout Inventory; ProQOL = Professional Quality of Life
Quantitative Analyses
We conducted correlations between the measures for each construct to evaluate which facets of each construct were associated with each other. All results should be interpreted within the context of a limited sample size (n=34). See Table 5 for correlational results across all quantitative measures.
Table 5.
Correlations Between Measures of Effectiveness, Connectedness, and Work-Related Well-Being.
| Measure | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1. STRS closeness | -- | ||||||||||||
| 2. STRS conflict | −0.18 | -- | |||||||||||
| 3. SEL comfort | 0.43* | −0.12 | -- | ||||||||||
| 4. CESS | −0.30 | 0.21 | −0.19 | -- | |||||||||
| 5. Advice network density | −0.29 | 0.30 | −0.17 | −0.27 | -- | ||||||||
| 6. Advice network betweenness | 0.23 | −0.01 | −0.08 | 0.15 | −0.37 | -- | |||||||
| 7. Communication Opportunities | 0.37* | −0.02 | 0.03 | −0.01 | −0.003 | 0.34 | -- | ||||||
| 8. UWES | 0.10 | −0.30 | 0.18 | 0.04 | −0.15 | 0.11 | 0.05 | -- | |||||
| 9. OBI disengagement | −0.12 | 0.26 | −0.38* | −0.23 | 0.59* | −0.27 | −0.26 | −0.46* | -- | ||||
| 10. OBI exhaustion | −0.03 | 0.37* | −0.47* | −0.11 | 0.47* | −0.04 | 0.03 | −0.56** | 0.76** | -- | |||
| 11. ProQOL compassion satisfaction | 0.40* | −0.30 | 0.43* | −0.03 | −0.25 | 0.24 | 0.19 | 0.48* | −0.51* | −0.38* | -- | ||
| 12. ProQOL burnout scale | −0.19 | 0.34 | −0.32 | 0.01 | 0.31 | −0.11 | −0.01 | −0.55** | 0.55** | 0.52* | −0.62** | -- | |
| 13. ProQOL secondary traumatic stress | 0.18 | 0.12 | −0.09 | −0.15 | −0.07 | 0.08 | 0.39* | −0.001 | 0.16 | 0.20 | 0.01 | 0.21 | -- |
Note: All correlations are pooled across 20 imputations to address missing data.
p<.05;
p<.001
Effectiveness and Work-Related Well-Being.
Higher reports of adult-youth closeness on the STRS were associated with higher reports of compassion satisfaction on the ProQOL scale (r =.401, p < .05). Higher reports of adult-youth conflict on the STRS were associated with higher reports of exhaustion on the Oldenburg Burnout Inventory (r = .368, p < .05). Greater comfort promoting social-emotional competencies among youth was associated with lower OBI disengagement (r = −.384, p <.05), lower OBI exhaustion (r = −.466, p < .05), and higher ProQOL compassion satisfaction (r = .428, p <.05).
Connectedness and Work-Related Well-Being.
There were no statistically significant associations (p>.05) between social support measured using the CESS and the work-related well-being outcomes. Greater communication opportunities were associated with higher reports of secondary traumatic stress, measured using the ProQOL scale (r = .389, p < .05).
There were no significant associations between advice network betweenness and work-related well-being. Advice network density was positively and significantly correlated with OBI disengagement (r = .588, p < .05) and exhaustion (r = .474, p < .05), such that great density, or network connections between the people in someone’s network, was associated with greater reports of burnout in the form of both exhaustion and disengagement. To better understand the association between advice network density and provider burnout (i.e., exhaustion and disengagement), we ran a series of follow-up analyses. First, we examined correlations between both disengagement and exhaustion with incoming advice ties, examining whether the number of people who come to providers for advice is associated with burnout. No significant associations were found. Next, we examined correlations between disengagement and exhaustion with reciprocal network advice ties, specifically the proportion of symmetric in-coming ties divided by the total number of in-coming ties. We found a significant and negative association, such that a greater percentage of people who come to providers for advice that they also go to in return is associated with lower disengagement (r = −.710, p < .001) and exhaustion (r = −.746, p < .001), emphasizing reciprocity as a potentially important aspect of advice interactions and bonding social capital (i.e., close ties with near colleagues).
Effectiveness and Connectedness.
There were limited associations between measures of effectiveness and connectedness, with only greater communication opportunities associated with higher reported of STRS adult-youth closeness (r = .369, p < .05).
Qualitative Analyses
We asked interviewees what parts of the job they find stressful (i.e., stressors), what they find helpful during stressful moments (i.e., coping/support factors), and what keeps them motivated and engaged in the work (i.e., motivating factors). To better understand the intersection between constructs, we identified stressors, motivating factors, and coping/support factors that overlap conceptually with either effectiveness or connectedness. Figures 2 and 3 reflect visual depictions of identified stressors and motivators across social-ecological levels.
Figure 2.
Stressors Identified by Qualitative Interview Participants (N=11) Across Ecological Levels.
Note. Font size corresponds to the number of interview participants who indicated that stressor, with larger font size reflecting more respondents indicating that code. Pink = Stressors associated with connectedness with colleagues. Orange = Stressors associated with perceived value of after-school by individuals outside of the after-school context. Green = Stressors associated with job demands outside of relationships with youth. Blue = Stressors associated with relationships with youth and youth engagement in programming.
Figure 3.
Motivators Identified by Qualitative Interview Participants (N=11) Across Ecological Levels.
Note. Font size corresponds to the number of interview participants who indicated that motivator, with larger font size reflecting more respondents indicating that code. Green = Motivating factors associated with aspects of the job outside of their relationships with youth. Blue = Motivating factors associated with relationships with youth and youth engagement in programming. Purple = Motivating factors associated with extrinsic rewards.
Effectiveness and Work-Related Well-Being.
We first examined effectiveness as a potential source of engagement for after-school providers. Eight out of 15 codes capturing sources of provider motivation were related to their interactions with youth. When asked what keeps providers motivated and engaged in the work, almost all interviewees’ first response was, “the kids.” Ten out of 11 providers noted enjoying the opportunity to connect and interact more with youth compared to during the school day (e.g., “Honestly, it’s just interacting with the kids. […] I don’t care if they want to sit there for the whole two hours and we have a conversation. As long as we’re interacting […]”). This additional time and opportunity to interact contributed to the capacity to build closer relationships with youth (reported by 73% of interviewees). With these closer relationships, providers reported that youth come to them more frequently for support, which 36% of interview participants reported enjoying (e.g., “I found that, for some reason, kids have a tendency to share and disclose information with me. And sometimes I serve a purpose just by being a listening ear. And I get gratitude in that.”).
Most providers (73%) also noted youth benefits from attending the program as a motivator. Providers (73%) reported enjoying their jobs most when youth are engaged (e.g., “Most of [the kids] would get involved and be interested in doing the activities that we had going on. And that was more fun for me too.”). Finally, providers (36%) indicated their own perceived effectiveness as a direct motivator (e.g., “I do it because I enjoy it because I’m good at it.”). Therefore, effectiveness supporting, engaging, and connecting with youth served as a salient contributor to positive work-related well-being in the qualitative interviews.
We also examined effectiveness supporting youth as a potential stressor or source of emotional strain. When asked what parts of the job they find difficult, the two most frequent responses were engaging youth in program activities and addressing youth mental health concerns, both of which were identified by seven out of 11 providers (e.g., “For me, the difficult part is keeping them, their minds active. I would say, keeping their minds and body active, getting them to play, to put down everything for the day and relax, enjoy.”). For five (45%) of the providers, part of this stress was due to feeling pressure, uncertainty, or limitations in addressing safety concerns as well as supporting youth around sensitive and/or emotionally-salient topics (e.g., “I felt a little guilty at times when [the student] had clearly said they were OK and everything was fine, but it was not.”). Therefore, feeling unable or unprepared to support youth was one common predictor of emotional strain identified across interviews.
Providers (45%) also mentioned sources of emotional strain related to handling high, changing, and/or conflicting job demands (e.g., “Oh, you’re supposed to supervise them to do the homework, but you can still do your paperwork. No, I can’t. If I’m doing my paperwork, it’s going to take me an hour to get my paperwork done because a child is asking for help.”); as well as stress from undesired parts of the job including paperwork (27%) and facilitating activities outdoors during intense heat (18%). When asked what providers find helpful during stressful moments, 45% indicated using action-based coping to address the source of their stress directly (e.g., “If you’re inside of the stress, quickly use your resources […] Get something that the kids can be active in, draw them in quickly and execute.”). Providers would therefore use their effectiveness and problem-solving skills to mitigate sources of stress.
Conversely, providers also gave evidence of work-related well-being as a facilitator of effectiveness. When asked what helps them be effective in their work and what recommendations they would give to future after-school providers, five out of 11 providers highlighted the importance of their own emotional and physical resilience (e.g., “I go back to the kids that we teach, you got to be mentally, mentally strong.”). Three of the providers (27%) highlighted the importance of having a deep commitment to the youth (e.g., “I don’t try to push them aside and say, oh, ask someone else to help you because I don’t know anything about it. We’re going to find out together.”). Therefore, having their own coping strategies and a strong engagement with the work and with youth were identified as ways to mitigate emotional strain such as stress and burnout.
Connectedness and Work-Related Well-Being.
Connectedness with colleagues was not indicated as a source of work engagement in the qualitative interviews. However, six (55%) participants indicated lack of connectedness with other colleagues as a source of strain, including lack of social support or specific behaviors that they found frustrating or stressful. Moving beyond other colleagues, a portion of providers (27%) highlighted communicating with parents and/or caregivers as a source of stress. Providers (45%) also found it stressful when others, including parents, kids, and other teachers in the day school, failed to see the value of the after-school program and the services that they offer (e.g., “I guess getting other teachers to understand that, you know, like this is not just fun and games, it is a program.”).
While connectedness, particularly lack of connectedness, sometimes served as a source of strain, providers also shared multiple examples of social support as a buffer against other sources of stress. When asked what other people do that they find particularly helpful during stressful moments, providers shared examples of instrumental support (55%; e.g., “[…] sometimes we call the manager, and our manager is very helpful. And once we call them, they come in and they assist you.”); informational support (55%; e.g., “And they help me look at it in a different, through a different pair of lens. There’s times I even come to them and say, listen, I have a biased approach because my environment, where I’ve come from, is different. Help me find a different approach.”); and emotional support (55%; e.g., “For me, it’s talking to someone about it. I feel that, you know, keeping it pent up or keeping it within, it’s not helping whatever the situation is and is not helping you.”).
Benefits of social support were amplified by having clear and consistent communication across team members (27%; e.g., “I think just having that relationship where you can actually talk to your peers and be like, hey, look, this is what’s going on and you’re checking in with them and you’re kind of all on the same page […]”); when coworkers and supervisors support them in front of others in a demonstration of solidarity (18%; e.g., “[…] maybe that teacher may have spoken with that child or, maybe in front of me, to let that child know that we’re all on the same team.”); having a consistent person or people to go to (18%); and working as a team with a shared mission and commitment to the youth (27%).
Providers reporting high satisfaction with their connectedness provided more examples of communication with individuals outside of their immediate after-school site. This included examples of communicating with day school teachers in the same school to support homework completion (e.g., “If you didn’t have homework, of course I’m the one who’s calling the teacher to find out. You’re not going to tell me you don’t have homework. I’m going to find out [laughter].”). Providers also shared examples of reaching out to other staff and teachers in the day school, particularly school counselors, for emotional support and reassurance after sensitive conversations with youth or when there were safety concerns (e.g., “We have counselors at the school, and plenty of times I might come and say, ‘hey, can I have a side bar with you for a second?”). Others shared the desire to have on-site mental health support for youth (e.g., “I think that part is missing from the after-school program part, having a hands-on counselor for the after-school sites.”). Finally, providers shared enjoying opportunities to attend program-level meetings and trainings where they could hear from providers at other sites and learn from external consultants brought in to support staff around particular program components.
Research Goal 3: Identifying Facilitators of and Barriers to Work-Related Well-Being
Based on qualitative frequencies and quantitative measures, providers were sorted into “low”, “medium”, and “high” well-being groups for each construct (i.e., emotional strain, compassion fatigue, and work engagement). To identify facilitators and barriers to work-related well-being, we examined differences in sources of strain, coping/support strategies, and motivators across the low well-being and high well-being constructs and groups. Differences between groups were examined qualitatively rather than quantitatively due to limited sample size.
Well-Being and Effectiveness
When discussing parts of the job they find difficult, providers reporting low well-being (i.e., high emotional strain, high compassion fatigue, and low work engagement), were more likely to communicate having difficulty engaging youth and parents, addressing youth problem behaviors, and building positive adult-youth relationships. However, it is unclear whether these difficulties were the result of low well-being or the cause of low well-being.
Providers in the high emotional strain and compassion fatigue groups were also more likely to report using coping strategies that create distance between them and the youth, including establishing boundaries (e.g., “You got to understand where you draw the line, how close you get […]”) and giving youth space (e.g., “I try not to bother them. I try not to be too hard on them because I don’t know what they going through. I don’t want to know what they going through because […] I’ll be crying all night long.”). Therefore, providers with high well-being were more likely to report effective and positive relationships with youth, while providers with high emotional strain and fatigue were more likely to maintain emotional distance in their interactions with youth.
Well-Being and Connectedness
Providers in the high work engagement and low emotional strain groups were more likely to report bonding social capital than providers in the low engagement and high emotional strain groups. For example, only one out of four providers in the “low engagement” group reported alignment in values and goals as well as the presence of respect and trust across their team, compared to three out of four providers in the “high engagement” group. More providers in the “high engagement” group also reported receiving instrumental support from their supervisors. These same differences were also found between the “high emotional strain” and “low emotional strain” groups. Providers with high engagement and low strain identified emotional support and a shared commitment to the youth as two of the most important forms of social support they receive from their coworkers during stressful moments.
Individual Coping Strategies
Providers also shared personal coping strategies for stressful moments on the job. Individuals reporting high levels of engagement and low levels of emotional strain were more likely to report using: (1) action-based coping (e.g., problem solving); (2) cognitive restructuring, including identifying alternative and coping thoughts, such as reminding themselves of the motivating parts of the job (e.g., “What I’ve been doing is, to alleviate the stress, is just to say, OK, this is part of it. Let me get it done before I have to interact with the kids.”); and (3) mindfulness strategies, including deep breathing, both on their own and with the youth (e.g., “OK, let’s just, five minutes, say it, stop what you’re doing, and breathe for five minutes […] We just stop and just do it together.”).
Work-Related Well-Being During COVID-19
After COVID-19 started shutting down schools, ASP’s sites adapted in multiple ways, with some shifting to remote programming and other sites pausing all programming until in-person services could resume. Due to interviews occurring across multiple months, some providers were interviewed while facilitating fully remote programming, some were interviewed while programming at their site was on hold, and some were interviewed after they returned to in-person programming following strict physical distancing and health guidelines. Throughout the interviews, we asked providers how their responses, including their experiences of effectiveness, connectedness, stress and engagement, changed or remained the same during COVID-19.
The most frequently reported stressors related to COVID-19 stemmed from perceived limitations in effectiveness. This included difficulties engaging youth in physically distanced programming, both in-person and remote (e.g., “What’s stressful is not being able to reach the ones at home, meaning like you really don’t know what they’re doing.”); acknowledging the loss of opportunities for youth (e.g., “Everyone has a different learning style […] those kids who are hands on learners, they’re going to be missing out.”); feeling restricted by resource limitations (e.g., “During COVID […] a lot of them didn’t have the materials they needed to do anything, even draw.”); and added concerns during remote programming about the safety and well-being of youth (e.g., “And that’s why I said I’m exhausted, because I’m monitoring these kids that are not in a space where they, some of them shouldn’t even be in that space.”).
Many providers also reported decreased connectedness with colleagues during COVID-19 due to most previous communication occurring in shared physical spaces, such as the hallway or lunchroom during free periods. During COVID-19, providers communicated more over text and Zoom, but that virtual communication was more prone to distractions and not as fulfilling as in-person communication (e.g., “I’d rather, I’m more someone who wants to be around people […] Like I want to be around these people, but I can’t. And that, it was very hard.”). Some providers shared that lack of effective communication during COVID-19 contributed to their stress (e.g., “You have to be transparent. Top, down, left, right, you have to be. Or else the person will feel as if they’re in this alone.”).
When sharing their sources of emotional strain during COVID-19, providers demonstrated a profound ability, time and again, to adapt and problem solve quickly, including identifying new resources (e.g., “We […] started looking at like Pinterest and Teachers Pay Teachers and different things where it kind of made it a little bit easier to figure out how to do some virtual stuff with the kids.”); finding strategies for maximizing youth engagement (e.g., “We find things that they can enjoy and play together. And usually it’s teachers versus the kids and they love that. So, we find different things to keep them engaged.”); and ensuring that youth knew that they care and are still there for them. This capacity to adapt is fueled in part by after-school providers’ deep commitment to supporting youth, highlighted in the following quote: “My attitude has not changed […] my attitude is not going to change. I’m not going to change because [of] COVID. I’m not going to not talk to them. I’m not going to not help them when they need. And that’s how it’s not going to change. Period.”
Discussion
This study used mixed methods to examine experiences of effectiveness, connectedness, and work-related well-being among after-school providers in a multi-site, middle-school age program serving predominantly diverse and low-income communities in a large city in the Southeastern United States.
Results indicated the importance of effectiveness for promoting positive after-school provider well-being, particularly their compassion satisfaction, and mitigating emotional strain, including burnout in the form of disengagement and exhaustion. Important aspects of provider effectiveness included their capacity to build close and positive relationships, promote social emotional skill development, and engage youth in programming. Difficulties engaging and supporting youth were the most frequently reported sources of emotional strain. Close relationships with youth and observing youth benefits from engaging in programming were the most frequently reported sources of engagement and motivation in the work.
Connectedness with colleagues demonstrated mixed associations with provider well-being. The absence of necessary support and ineffective communication served as sources of emotional strain, but the presence of trusting and mutually supportive relationships with colleagues helped to buffer experiences of emotional strain, including burnout (i.e., disengagement and exhaustion). We reflect on these findings below.
Effectiveness and Well-Being
Results across quantitative and qualitative data strands demonstrate the importance of effectiveness, particularly effectiveness engaging and developing close and positive relationships with youth, when predicting and promoting work-related well-being for after-school providers. While quantitative measures of effectiveness were constrained by ceiling effects, quantitative analyses still found significant associations between provider effectiveness and well-being. Specifically, comfort facilitating youth social-emotional skill development was associated with higher compassion satisfaction and lower burnout (both disengagement and exhaustion), and ability to develop close relationships with youth was associated with lower burnout (both disengagement and exhaustion). These associations were confirmed by qualitative reports of motivating factors in the work, which centered around providers’ opportunities to build positive, supportive relationships with youth, aligning with sources of job satisfaction reported in previous studies with after-school providers (Hwang et al. 2020). Therefore, important professional development opportunities for after-school providers to promote both their effectiveness and work-related well-being are mentorship and relationship building skills with youth and opportunities for training and implementation support on social emotional learning programming (Frazier et al., 2019).
Conversely, reports of adult-youth conflict served as a primary predictor of emotional strain, including stress and burnout, particularly exhaustion, across both quantitative and qualitative reports. Qualitative feedback also highlighted lack of youth engagement in programming and disruptive behaviors as prominent sources of stress not captured by the quantitative measures. This presents a second professional development opportunity for after-school providers, including strategies for decreasing adult-youth conflict and making programming more engaging for youth, such as participative decision-making that enables youth to contribute to programming format and selection (Akiva et al., 2014).
While close relationships with youth presented an important source of provider engagement, interview participants also highlighted emotional strain, including stress and compassion fatigue, when youth disclosed sensitive information. Providers highlighted the emotional impacts on their own well-being as well as stress due to not knowing or feeling able to get youth the support they need. Participants varied in the extent to which they reported having the individual skills (e.g., stress management and emotional regulation skills) and social support (e.g., emotional support for coping and instrumental support for addressing youth concerns) they needed and wanted.
Providers’ variable approaches to coping with stressful situations potentially contributed to their variable reports of closeness and conflict with youth. Providers expressing high levels of emotional strain, including compassion fatigue, demonstrated two primary methods for coping. One group indicated withdrawal from youth and a reluctance to get too close in fear of hearing sensitive or distressing information. The other group provided more examples of reaching out to others, including supervisors and school counselors, to meet youth needs. Providers who shared more examples of having people to go to when they had safety concerns were more likely to qualitatively and quantitatively report close and positive relationships with youth. However, providers who indicated more withdrawal responses when youth disclosed sensitive information were more likely to report examples of adult-youth conflict.
Across the quantitative and qualitative results, associations between adult-youth relationships and after-school provider well-being highlight that (1) close relationships with youth can serve as a motivator for after-school staff but also a source of emotional strain due to the increased information youth share as a result of these relationships; (2) stress due to supporting youth can contribute to adult-youth conflict and further withdrawal from relationships, as previously found among school day teachers (e.g., Yoon, 2002); and (3) it is important for after-school providers to have support from others, including supervisors and mental health professionals, to address youth needs that arise as a result of close adult-youth relationships. Therefore, connectedness has the potential to amplify the benefits of effective adult-youth relationships, while also mitigating the risks that can come from these relationships.
Connectedness and Well-Being
While connectedness served as a protective factor against stressors from supporting youth, it presented mixed associations with work-related well-being. Interestingly, the quantitative analyses highlighted increased communication opportunities as a potential risk factor for increased emotional strain, with greater program and site-level communication opportunities associated with higher reported secondary traumatic stress. This may be due in part to providers sharing with each other about stressful information or stories about youth, increasing the likelihood that providers hear about potentially traumatic events youth are experiencing. In addition to creating spaces to share, programs may benefit from creating shared opportunities to learn and practice coping strategies for managing secondary traumatic stress (e.g., Boatswain, 2023) within a supportive culture and climate (State et al., 2024).
Providers reporting more burnout also shared more examples of “venting” with colleagues, including expressing frustration about youth or parents. Conversely, providers with higher levels of well-being expressed more examples of collective problem solving to meet youth needs. Therefore, in addition to creating collective spaces to learn individual coping and stress management strategies, teams can also benefit from learning and applying collective problem-solving practices.
Additionally, advice network density, or the extent to which the people someone goes to for advice also go to each other, was associated with higher reported burnout, including both exhaustion and disengagement. This is contrary to previous research in other settings, which has highlighted density as a metric of bonding social capital (i.e., strong ties; Neal & Neal, 2019), with positive associations between bonding social capital and experiences of work-related well-being (Huang & Liu, 2017). This result may have been impacted by the use of ego networks, resulting in potentially incomplete site-level networks and restricted measures of density.
However, Edinger & Edinger (2018) also identified a negative association between advice network density and another construct of work-related well-being, job satisfaction, in a sample of 122 elementary school teachers. While greater advice network density was associated with lower job satisfaction in their study, they found that greater reported trust in colleagues was associated with higher levels of job satisfaction. They predicted that these results reflect that highly dense advice networks may create a communication burden for individuals, particularly when ineffective communication is used, and that frequent advice-oriented connections do not necessarily result in trusting relationships. These predictions align with qualitative feedback from the current study, with ineffective communication and a lack of instrumental (i.e., direct) support towards meeting job demands frequently reported by providers expressing low engagement and high emotional strain. These results indicate that the quality and type of interactions may matter more than advice ties for understanding work-related well-being.
When we explored further associations between participants’ advice networks and their reported well-being, we found that reciprocity was a significant predictor of burnout, such that providers with more reciprocal advice relationships, including going to the people who also come to them, reported lower burnout. Therefore, reciprocity in advice interactions may be a stronger metric of trust and bonding social capital than advice density, particularly within the context of work-related well-being. This finding reinforces that collective problem-solving practices have the potential to promote work-related well-being by creating opportunities for shared, and ideally reciprocal, learning within teams.
Alternatively, when asked what others do that they find helpful during stressful moments, providers highlighted emotional support, information or assistance towards meeting job demands (i.e., instrumental and informational support), and having a consistent team of colleagues who effectively communicate and share a commitment to serving youth. Qualitative examples of bonding social support came up most frequently across providers reporting both high effectiveness and high well-being, emphasizing that, while social network density was not associated with positive work-related well-being in this sample, they still found it particularly helpful if there was a perception of trust, respect, and shared goals across colleagues. Providers communicated the utility of having a small group of people that they can go to regularly for support and the importance of having a co-facilitator for sharing the workload and exchanging ideas. These results highlight the importance of promoting a positive organizational culture and climate to facilitate both provider effectiveness and well-being (Ouellette et al., 2020).
When understanding the role of bridging social capital and work-related well-being, social network metrics of bridging social capital, particularly betweenness (Neal & Neal, 2019), were not associated with either burnout or engagement. Qualitative interviews provided additional context regarding how to optimize bridging social capital within after-school programs. Multiple providers described lack of effective communication and connections with day school teachers as a source of emotional strain, particularly when this lack of communication resulted in difficulties facilitating youth homework completion or day school teachers pulling youth from the program as a disciplinary measure.
Additionally, when seeking out emotional support for themselves or their students, multiple providers indicated reaching out to school counselors for advice, reassurance after difficult conversations, or to connect youth with services. One provider emphasized that, because school counselors are not usually there during after-school hours, it would be particularly helpful to have on-site or on-call mental health support for youth during after-school hours. There are multiple means in which programs may accomplish this, including recruiting and compensating day school mental health staff to continue for a portion of the after-school period, identifying community mental health providers who provide telehealth services as needed, using free call lines such as those offered by SAMHSA to connect students to referrals or crisis support as needed, and collaborating with local universities with mental health training programs.
For example, Frazier et al. (2015) developed a model in which social work students received field placements in after-school programs to support implementation of mental health prevention and promotion programming as a practicum experience during graduate school. Such models can increase mental health supports and bridging social capital for after-school programs without incurring extensive additional costs. Bridging social capital therefore presents opportunities to mitigate stress for after-school providers by increasing their effectiveness supporting youth and connecting youth with important services.
A Social-Ecological Understanding of Work-Related Well-Being in After-School
Participating after-school providers indicated sources of emotional strain and motivation (i.e., engagement) across ecological levels, including individual coping strategies (e.g., action-based coping, cognitive restructuring, and mindfulness). Across the microsystem level, providers emphasized the importance of youth engagement, positive adult-youth relationships, effective communication with the parents of enrolled youth, and social support (i.e., instrumental, informational, and emotional) from coworker and supervisors. At the mesosystem level, providers highlighted the utility of connections with day school teachers and counselors in facilitating homework completion and meeting youth mental health needs. At the exosystem level, providers highlighted the importance of adequate funding and resources for meeting youth needs and maximizing engagement, with limited financial resources restricting providers’ capacity to introduce youth to new opportunities. Finally, the macrosystem level focuses on an overarching lack of value given to after-school, similar to the experiences of teachers (Price & McCallum, 2015), with after-school providers indicating a lack of perceived value in after-school by youth, parents, and other day school teachers. Each ecological level offers a deeper and more contextualized understanding of work-related well-being for after-school providers, while highlighting future pathways for intervention and support.
Limitations
It is important to note that these results are constrained by a small sample size. Small sample sizes were due in part to recruiting during the COVID-19 pandemic, especially because after-school providers in the collaborating program also served as day school teachers and were navigating continuous pandemic-related changes and demands. These considerations resulted in six ASP sites not contributing any survey data, potentially biasing the results towards sites and providers with higher well-being and fewer data collection barriers. Additionally, the small sample size presents increased risk for Type I and Type II errors in the correlational analyses. We do not recommend interpreting individual quantitative results, but rather focusing on trends for each construct across qualitative and quantitative strands.
Due to the limited sample size spread across a large number of sites, we were not able to account for variation at the site level within the quantitative analyses. This also prohibited us from using site level social network metrics, instead relying on ego networks. Intraclass correlations were above .10 for STRS closeness, SEL comfort, and ProQOL compassion satisfaction, indicating the importance of accounting for site-level differences in future studies with larger samples. Additionally, future studies should examine similar constructs in after-school programs located in different geographic locations, as results from the current study may only generalize to similar programs.
Quantitative measures of effectiveness and work engagement were also constrained by ceiling effects, potentially resulting from response bias. Conversely, there were floor effects in reports of stress during qualitative interviews, potentially due to variable levels of comfort talking about emotionally-salient topics. Finally, the OBI disengagement subscale (α = .60) and ProQOL secondary traumatic stress subscale (α = .52) demonstrated low internal consistency, potentially due to variable interpretation and/or comfort responding to the survey items. The measures with ceiling and floor effects or low internal consistency were typically less likely to be correlated with other variables, likely due to limited variation and/or consistency in responses. Correlations with these variables should be interpreted with these considerations in mind. However, these restrictions are mitigated in part by our use of mixed methods, with complementary strengths and weaknesses across data strands.
Due to the cross-sectional nature of the data, causal or directional associations cannot be differentiated. Particularly for associations between effectiveness and work-related well-being, both quantitative and qualitative results indicated potential for a bidirectional relationship. Additionally, despite the use of repeated survey prompts for providers to think specifically about their experiences with in-person programming, recall bias and the COVID-19 pandemic may have impacted providers’ responses in a way that detracted from our ability to fully generalize to traditional in-person programming. Finally, interpretation of the results, particularly the qualitative findings, may have been influenced by the team’s theoretical leaning as clinical psychologists. Future studies would benefit from use of member checking to confirm for correct interpretation of feedback.
Conclusions and Future Directions
Results, collected using mixed methods, highlight nuanced associations between effectiveness supporting youth, connectedness with coworkers, and work-related well-being for after-school providers. While opportunities to interact with and develop bonds with youth was the primary motivator shared by staff, these relationships also came with increased concerns about the well-being of youth who may be in unsafe or unhealthy environments. Similarly, increased social support and bonding social capital, particularly a shared commitment to serving youth, presented as buffers against stress, but increased communication also came with increased risk of hearing potentially stressful information, particularly in cases where staff didn’t feel personally or collectively able to address youth needs. These trends present multiple areas for further study with larger sample sizes, as well as potential strategies for promoting the work-related well-being of after-school staff. Identified opportunities include having on-site mental health counselors available to address youth mental health concerns (an example of increased bridging social capital) and implementing organizational and site-level goal alignment and team-based decision-making interventions, such as those identified in Ouellette et al. (2020), to further build a sense of trust and shared vision (an example of increased bonding social capital).
The nuanced relationships between effectiveness, connectedness, and work-related well-being would not have been captured without the use of mixed methods. Therefore, continued application of mixed methods in examining associations between and changes in effectiveness, connectedness, and work-related well-being in response to social-ecologically-informed professional development and intervention efforts presents a critical next step in advancing our understanding and promotion of work-related well-being in after-school. Overall, the current study highlights the importance of the social-ecological context in promoting work-related well-being for after-school staff. While most interventions, particularly in the school context, for promoting work-related well-being have focused on individual factors, including stress management, self-care, and mindfulness strategies (e.g., de Jesus et al., 2014; Flook et al., 2013), it is important to also look beyond individuals to their surrounding context (State et al., 2024). Organizational interventions and workforce support opportunities that offer resources across social-ecological levels present an important alternative approach to promoting work-related well-being for after-school providers.
Supplementary Material
Acknowledgements:
This research was supported in part by the National Institute of Child Health and Human Development (F31 HD100156) and a Peter and Malina James & Dr. Louis P. James Legacy Scholarship from American Psychological Foundation/Council of Graduate Departments of Psychology. Thank you to Drs. Elisa S. Shernoff and Jennifer Watling Neal for their invaluable feedback on the data collection and analytic design, as well as Drs. Valentina Bruk-Lee, Daniel Bagner, and Maria Elena Villar for their feedback as members of Dr. Ouellette’s dissertation committee.
Footnotes
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Declarations of interest: none
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