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. Author manuscript; available in PMC: 2025 Feb 8.
Published in final edited form as: Evid Based Pract Child Adolesc Ment Health. 2024 Feb 8;9(1):15–32. doi: 10.1080/23794925.2024.2310512

Outside the Clinic Door: How Neighborhood Sociodemographic Disadvantage is Associated with Community Therapist Secondary Traumatic Stress and Emotional Exhaustion

Mary Kuckertz 1, Teresa Lind 2,3, Anna S Lau 4, Mojdeh Motamedi 3,5, Blanche Wright 6,7, Kenny Le 8, Lauren Brookman-Frazee 3,5
PMCID: PMC10906964  NIHMSID: NIHMS1962681  PMID: 38435098

Abstract

Therapists serving families with high rates of trauma exposure in community mental health clinics face the potential risk of experiencing secondary traumatic stress and emotional exhaustion, both of which pose barriers for the implementation and sustainment of evidence-based practices. Previous research documents negative effects of living in socioeconomic disadvantaged neighborhoods on child development but has not examined the effects of working in these neighborhoods on therapist well-being. The current study merges publicly available data, administrative claims data on mental health services, and therapy survey data to 1) identify associations between neighborhood sociodemographic disadvantage and two community therapist well-being constructs, specifically secondary traumatic stress and emotional exhaustion; and 2) examine potential clinic- and therapist-level explanatory factors in the associations between neighborhood sociodemographic disadvantage and therapist well-being. A cumulative risk index approach was applied to calculate neighborhood sociodemographic disadvantage. Greater neighborhood sociodemographic disadvantage was significantly associated with higher levels of therapist secondary traumatic stress (B=.09, p<.05) but not emotional exhaustion. Because therapists in higher risk neighborhoods face higher secondary traumatic stress levels, additional research is needed to better understand how these therapists can be best supported; thus, supporting families receiving treatment and implementation of evidence-based practices.

Keywords: secondary traumatic stress, emotional exhaustion, neighborhood risk, implementation, evidence-based practice, community mental health

Therapists face many challenges in delivering evidence-based mental health practices in community settings. These include learning and implementing new evidence-based mental health practices (EBPs) in addition to managing high caseloads. According to the Exploration, Preparation, Implementation, Sustainment (EPIS) framework, implementation of EBPs are influenced by factors in both the inner context (such as therapist characteristics or the organizational climate of the clinic), as well as factors in the outer context (such as the service environment or funding policies; Aarons et al., 2011). Several studies have found associations between therapist and agency inner context factors and therapist well-being (Brookman-Frazee et al., 2018; Kim et al., 2018; Rodriguez et al., 2018; Thompson et al., 2014). However, there is less work examining the influence outer context factors have on therapist well-being, such as the neighborhood environment surrounding a therapist’s agency or clinic. Therefore, this study aims to investigate associations between clinic neighborhood sociodemographic disadvantage and two community mental health therapist well-being constructs, specifically secondary traumatic stress and emotional exhaustion.

Therapist Well-Being and Large-Scale Implementation of Multiple EBPs

Large-scale implementation efforts of EBPs are an increasingly common method used to improve community mental health services for children. Though these initiatives often increase the initial delivery of EBPs, they face challenges ensuring the sustained delivery, or continued use, of the EBPs after the initial implementation push (Stirman et al., 2012). A key component for sustained delivery is having a trained and reliable workforce of therapists to provide the EBPs. However, sustained delivery of EBPs is threatened by the high rates of therapist turnover in community clinics, which lose 30% to 60% of their workforce annually (Beidas et al., 2016; Bukach et al., 2017; Mor Barak et al., 2001). Therefore, it is critical to understand therapist stressors that have been linked to turnover and discontinued delivery of EBPs. These include both secondary traumatic stress, which is the emotional duress that results when an individual hears about the firsthand trauma experiences of another (Bride et al., 2004; Cieslak et al., 2014), and burnout, which consists of the physical, emotional and mental exhaustion that results from long-term involvement in work situations that are emotionally demanding (Maslach & Goldberg, 1998; Schaufeli & Greenglass, 2001). Understanding determinants of these therapist stressors is critical to promoting therapist tenure at clinics and their sustained delivery of EBPs.

Context: Large-Scale Implementation of Multiple EBPs in Los Angeles County

To explore correlates of therapist secondary traumatic stress and emotional exhaustion, this study examined therapists from 112 clinics that were directly operated by or contracted with the Los Angeles County Department of Mental Health (LACDMH), the largest county mental health department in the United States, to provide mental health services for children. As part of an initiative to increase EBP delivery in community settings, the Prevention and Early Intervention (PEI) initiative therapists working in the LACDMH system received training in multiple EBPs (Lau et al., 2021) and were expected to deliver specific EBPs to receive reimbursement through the PEI funding stream (Los Angeles County Department of Mental Health, 2009). The PEI initiative provided training and support for specific EBPs, including Cognitive Behavioral Intervention for Trauma in Schools (CBITS), Child-Parent Psychotherapy (CPP), Interpersonal Psychotherapy for Depression (IPT), Incredible Years (IY), Managing and Adapting Practice (MAP), Parent-Child Interaction Therapy (PCIT), Seeking Safety (SS), Trauma-Focused Cognitive Behavior Therapy (TF-CBT), and Positive Parenting Program (Triple P). Though the current study was conducted approximately ten years after the initiative began, large numbers of therapists were continuing to be trained in these EBPs and to deliver them for PEI reimbursement.

Conceptual Framework of EBP Sustainment

This study was guided by the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. According to this model, the implementation and sustainment of EBPs are influenced by factors in both the inner context (such as therapist characteristics or the organizational climate of the clinic), as well as factors in the outer context (such as the service environment or funding policies; Aarons et al., 2011; Bond et al., 2014; Proctor et al., 2015). As previously discussed, therapist factors such as secondary traumatic stress and emotional exhaustion have been found to be associated with shortened therapist tenure and discontinuation of EBP delivery (Bukach et al., 2017; Ducharme et al., 2007; H. Kim & Stoner, 2008; Lau et al., 2020). Therefore, it is important to consider factors in both the inner context and outer context that may contribute to therapist secondary traumatic stress or emotional exhaustion and to the long-term sustained delivery of EBPs.

Factors Associated With Therapist Secondary Traumatic Stress and Emotional Exhaustion: Inner Context

Inner context factors have been found to be associated with both therapist secondary traumatic stress and emotional exhaustion (Bercier & Maynard, 2014; Hensel et al., 2015; J. J. Kim et al., 2018; Penix et al., 2019). Previous meta-analytic research shows that the correlation between secondary traumatic stress and emotional exhaustion is high (weighted r of .69; Cieslak et al., 2014) with separate research showing that the two constructs may exacerbate each other (Malkina-Pykh, 2017). Some researchers have argued that emotional exhaustion is one of the largest predictors of secondary traumatic stress (Shoji et al., 2015; Temitope & Williams, 2015) but that secondary traumatic stress does not predict emotional exhaustion (Shoji et al., 2015). Given that these two constructs appear to be heavily connected, it follows that these constructs should be discussed in conjunction with each other.

Inner context factors at both the therapist level and the clinic level have been found to be associated with therapist secondary traumatic stress and emotional exhaustion (Bercier & Maynard, 2014; Hensel et al., 2015; J. J. Kim et al., 2018; Penix et al., 2019). In particular, the characteristics of therapists’ caseloads have been linked to secondary traumatic stress and emotional exhaustion, with therapists attending to a higher proportion of trauma cases experiencing greater levels of secondary traumatic stress (Bercier & Maynard, 2014; Bride et al., 2007; Hensel et al., 2015; J. J. Kim et al., 2018; Quinn et al., 2018). This is especially relevant for community therapists, as a significant portion of children are exposed to traumatic events, and these children often present to community clinics for mental health treatment (Baglivio et al., 2015; Saunders & Adams, 2014). It is estimated that approximately 30% of youth have experienced at least one traumatic event, and that statistic rises to 51% among Hispanic children (Sacks & Murphey, 2018), the largest ethnic group receiving community mental health services in Los Angeles County (Los Angeles County Department of Mental Health, 2018). Even when the presenting problem is not trauma, therapists still may be working with youth exposed to potentially traumatic events given the high rates of trauma nationwide (National Institute of Mental Health, 2017; Saunders & Adams, 2014; U.S. Department of Veterans Affairs, 2019). Other therapist-level factors that contribute to higher levels of secondary traumatic stress include personal trauma history and lack of peer support (Bride et al., 2007). Additional therapist-level factors contributing to emotional exhaustion include weekly work hours and the number of EBPs the therapist delivers (J. J. Kim et al., 2018).

Clinic level factors can also influence community therapist secondary traumatic stress levels and emotional exhaustion. Lower secondary traumatic stress is seen in therapists who have high levels of peer support (Choi, 2011; Hensel et al., 2015). Organizational context also appears to be related to secondary traumatic stress in that receiving higher levels of leader support, rating one’s relationship with supervisors positively, and access to the organization’s strategic plan are linked to lower levels of secondary traumatic stress (Choi, 2011; Penix et al., 2019; Quinn et al., 2018), with high perceived levels of autonomy in the workplace and quality performance feedback being linked to lower levels of emotional exhaustion (Morse et al., 2012). Given the role that organizational leadership and clinic environment can play in affecting therapist well-being (Choi, 2011; Penix et al., 2019; Quinn et al., 2018), it follows that altering therapist caseload composition and improving organizational climate may be linked to therapist secondary traumatic stress and emotional exhaustion above and beyond therapist professional background characteristics.

Factors Associated With Secondary Traumatic Stress and Emotional Exhaustion: Outer Context of Neighborhood Sociodemographic Disadvantage

Unlike the links between inner context factors, therapist secondary traumatic stress, and emotional exhaustion, there are few studies examining outer context factors that may influence therapist well-being (McHugh et al., 2020; Pulido, 2012; Schuler et al., 2016). One key factor in the outer context is the level of sociodemographic disadvantage in the neighborhood in which a clinic is located. Neighborhood sociodemographic disadvantage identifies those geographic areas most likely to confer psychological risk to residents due to the overall lack of social and material resources in the neighborhood. Exposure to risk, including living in a neighborhood with high levels of community violence has been linked to more PTSD-like symptoms in both adults and youth (Cecil et al., 2014; Pinderhughes et al., 2015). Therefore, therapists working in clinics in more disadvantaged neighborhoods are theorized to be at a higher risk for secondary traumatic stress (Rauvola et al., 2019). It is also worth noting that therapists working at community mental health clinics often face high levels of administrative work including insurance billing, heavy caseloads, and low levels of compensation (Kim et al., 2018; Lemak et al., 2003; Ryan et al., 2019) in addition to their own personal financial stressors (Last et al., 2022). It is possible that working in a community mental health setting may exacerbate stress related to clinical duties including secondary traumatic stress and emotional exhaustion. In addition, a high concentration of government funded community mental health clinics in a given area suggests that residents living in that neighborhood may face higher levels of socioeconomic stress as indicated by a high need for low-cost mental health services, although empirical research in this area would be beneficial.

In the current study, we apply a cumulative risk approach typically used to assess risk in children to characterize neighborhood sociodemographic disadvantage. Specifically, disadvantage was indexed by the following factors: higher population density, lower household income, lower parental education, and more single parent families. Each of these factors have been found to be associated with mental health risk and evaluated as part of previous cumulative risk indices (Doan et al., 2012; Mackenzie et al., 2011; Pungello et al., 2010; Wells et al., 2010). Specifically, living in an area with high population density has been associated with difficulties in school for youth (Roy & Raver, 2014), feelings of learned helplessness (Evans & Cassells, 2013) and an increased risk for psychosis (Lewis et al., 2020). Low household income is associated with stress dysregulation in youth (Kim et al., 2016), depression, problems at school, and problematic externalizing behaviors (Taylor et al., 2014). Low parental education is associated with anxiety, depression (van Beljouw et al., 2010), and externalizing behavior problems in youth (Schindler et al., 2015). Additionally, youth growing up in single parent families face increased levels of behavior problems (Roy & Raver, 2014) and parents face increased risk for anxiety and depression (Liang et al., 2019).

Current Study

The current study has two primary aims. First, this study aims to identify associations between neighborhood sociodemographic disadvantage and two therapist well-being constructs, specifically therapist secondary traumatic stress and emotional exhaustion. It is hypothesized that greater neighborhood sociodemographic disadvantage will be associated with greater therapist stress and greater emotional exhaustion (Aim 1). Second, given the role that clinics and organizational leadership can play in impacting therapist well-being (Choi, 2011; Penix et al., 2019; Quinn et al., 2018), this study aims to examine potential clinic and therapist level explanatory factors in the associations between neighborhood sociodemographic disadvantage and two therapist well-being constructs, specifically therapist secondary traumatic stress and emotional exhaustion (Aim 2). It is hypothesized that clinic level, inner context variables will account for a significant amount of the variance in secondary traumatic stress controlling for percentage trauma caseload, emotional exhaustion, and organizational cohesion. Similarly, it is hypothesized that clinic level, inner context variables will account for a significant amount of the variance in emotional exhaustion controlling for percentage trauma caseload, secondary traumatic stress, and organizational cohesion. To the authors’ knowledge, this will be the first study to examine community mental health therapist well-being in the context of outer neighborhood environment.

Methods

Procedure

The current study utilized three data sources: 1) survey data collected in 2019 from therapists delivering PEI EBPs, 2) administrative claims data for all PEI claims made into the LACDMH system during the 2017–2018 fiscal year, and 3) census data for Los Angeles County divided into neighborhoods. Study procedures were approved by LACDMH, University of California, Los Angeles, and University of California, San Diego prior to study commencement.

Therapist Survey Data

Therapists were eligible to complete the survey if they worked at an agency directly operated or contracted by LACDMH that delivered at least one PEI EBP. Therapists were emailed an invitation to participate in the survey. If interested, therapists were sent a link to provide informed consent and complete survey items online. Therapists received $20 for completing the survey and an additional $20 for completing the survey within a specific timeframe. Survey data were collected between January 2019 through July 2019. The final sample included 560 community therapists from 112 clinics in 60 neighborhoods who completed all portions of the survey included in the current study. Ninety-four percent (n=527) of the sample had training in at least one trauma focused EBP. Therapists reported clinic at which they worked at the time of the survey. If therapists worked at multiple clinic locations, the location from which the therapist worked most frequently was used for the purposes of data collection. There was a response rate of 32% after bounce back emails were removed from the calculation. However, this estimate is likely an underestimate of the true response rate as there were likely many therapists with inactive email addresses that did not bounce back (Some clinic email systems deactivated email accounts after a period of time and sent bounce back messages when contacted. Other clinic email systems did not have this in place, so no bounce back messages were received). For some therapists, it had been multiple years since study staff had corresponded with the therapist at the given email address on file, and it is possible turnover may have played a role in email addresses becoming inactive. There was a mean of 5.00 (SD=5.67) participating therapists per clinic. See Table 1 for demographic data.

Table 1.

Therapist Characteristics (n = 560)

Characteristics
Therapist Gender, No. (%)
 Female 501 (89.5)
 Male 59 (10.5)
Therapist Age
 M (SD) 36.1 (9.1)
  Range 20–74
Therapist Race No. (%)
 White 227 (40.5)
 Black or African American 49 (8.8)
 American Indian or Alaska Native 8 (1.4)
 Asian or Asian American 43 (7.7)
 Native Hawaiian or Pacific Islander 2 (.4)
 Middle Eastern or North African 11 (2.0)
 More than One Race 44 (7.9)
 Other 176 (31.4)
Therapist Ethnicity, No. (%)
 Hispanic 316 (56.4)
 Not Hispanic 244 (43.6)
Therapist Education, No. (%)
 Less than Master’s degree 13 (2.3)
 Master’s degree 498 (88.9)
 Doctoral degree 49 (8.8)
Therapist Discipline, No. (%)
 Marriage and family therapy 310 (55.4)
 Psychology/counseling 64 (11.4)
 Social work 181 (32.3)
 Other 5 (.9)
Therapist Language, No, (%)
 Can deliver services in English only 189 (33.8)
 Can deliver services in a non-English language 371 (66.3)
Therapist Licensure, No. (%)
 Licensed 228 (40.7)
 Not licensed 332 (59.3)
Therapist Theoretical Orientation, No, (%)
 Cognitive Behavioral or Behavioral 357 (63.7)
 Not Cognitive Behavioral or Behavioral 203 (36.3)
Years of Practice at Current Clinic
 Mean (SD) 3.9 (3.9)
  Range 0–26
Number of Clients on Therapist’s Caseload
 Mean (SD) 16.8 (9.2)
  Range 0–88
Percentage of Therapist’s Caseload Trauma
 Mean (SD) 24.2 (24.3)
  Range 0–100
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Notes. EBP=evidence-based practice.

Administrative Claims Data

All administrative billing claims submitted to the PEI funding stream during the fiscal year 2017–2018 were extracted. These data provided the geographic location (i.e., address) of all clinics directly operated or contracted by LACDMH that delivered PEI services during this time period, which helped study staff in findings which neighborhood each participating therapist delivered services most frequently. A total of 351 clinics were identified within 104 larger agencies. Administrative claims were matched with the therapist survey data through national provider identifiers (NPIs) registry.

Neighborhood Census Data

United States Census data at the neighborhood level was obtained from the Los Angeles Times “Mapping L.A.” project (Los Angeles Times, 2020; “Mapping L.A.” by Los Angeles Times is licensed under CC BY 2.0; https://creativecommons.org/licenses/by-sa/2.0/legalcode). To ensure license compliance, neighborhood sociodemographic disadvantage scores can be found in Appendix 1. The “Mapping L.A.” project categorizes census data in a unique way that is particularly relevant for studying neighborhood factors. Rather than using the census tracts drawn by the U.S. Census Bureau, the “Mapping L.A.” project adjusted and merged census tracts into the geographic unit of neighborhoods. This designation was informed by the historical, geographical, and socioeconomic associations that define these communities, and were refined based on reader feedback over the years since the project launched in 2009. A total of 266 neighborhoods within Los Angeles County have been identified by the “Mapping L.A.” project and linked to census data collected in 2000. Of these, 121 neighborhoods had at least one clinic billing for PEI, and 145 neighborhoods did not have a clinic. The therapists that participated in this survey reflect 60 of the 121 different neighborhoods with at least one clinic billing for PEI. Neighborhoods with LACDMH contracted clinics with therapists from the survey sample did not differ from neighborhoods with LACDMH contracted clinics for which therapists did not participate in the survey (independent samples t-test conducted; population density, t(119) = −.02, p = .99; household income, t(119) = .84, p = .41; four-year degree, t(119) = −.14, p = .89; and single parent families, t(119) = −.51, p = .61). There was a mean of 9.28 (SD=11.70) clinics with participating therapists per neighborhood.

Measures

Neighborhood Sociodemographic Disadvantage (Independent Variable)

A cumulative risk model was used to measure neighborhood sociodemographic disadvantage, based on evidence that the combination of risk factors is greater than simply the sum of individual risk factors (Appleyard et al., 2005; Brown & Ackerman, 2011). Therefore, a cumulative risk model captures a larger picture of risks in one’s environment rather than individual risk components (Brown & Ackerman, 2011). Demographic census data at the neighborhood level was obtained from the “Mapping L.A.” project. The following neighborhood risk variables were collected: population density, median household income, education, and single parent family status (Doan et al., 2012; Mackenzie et al., 2011; Pungello et al., 2010; Wells et al., 2010). The percentage of housing occupied by renters was not included in the index of neighborhood sociodemographic disadvantage due to mixed findings in the literature suggesting that mortgage payments associated with home ownership can serve as both a stressor and protective factor (Evans et al., 2003; Waldron & Redmond, 2017).

In order to set the stage for the current study and to empirically understand the level of sociodemographic disadvantage within our sample prior to examining sociodemographic disadvantage in conjunction with other constructs, neighborhoods with and without clinics across the eight service areas in Los Angeles County (Los Angeles County Department of Mental Health, 2023) were compared. There was significant variability across population density, household income, four-year degree, and single parent family status in the full sample of Los Angeles County neighborhoods. See Supplemental Table 1 for more detail. Neighborhoods with a publicly funded mental health clinic had significantly higher levels of sociodemographic disadvantage compared with neighborhoods without a clinic (independent samples t-test conducted; population density, t(264) = −3.98, p < .01; household income, t(264) = 5.82, p < .01; four-year degree, t(264) = 3.95, p < .01; and single parent families, t(264) = −4.73, p < .01).

To calculate the cumulative risk score for neighborhoods with a clinic, the average for the each of these neighborhood risk components across all L.A. neighborhoods was calculated. To be consistent with approaches used in previous literature, higher risk was operationalized as one standard deviation away from the mean of each variable (Appleyard et al., 2005; Brown & Ackerman, 2011; Ettekal et al., 2019), with the direction dependent on the relationship suggested by the literature (Appleyard et al., 2005; Brown & Ackerman, 2011; Ettekal et al., 2019). Each of the neighborhood characteristics functioned as a risk component for these purposes, with a 0 indicating absence and 1 indicating presence of the risk component. A cumulative risk score was calculated from the sum of these risk components.

Therapist Secondary Traumatic Stress (Dependent Variable)

Therapist secondary traumatic stress was measured via the Intrusion subscale of the Secondary Traumatic Stress Scale (Bride et al., 2004), which consisted of 5 items (α=.82; e.g., “It seemed as if I was reliving the trauma(s) experienced by my client(s),” “Reminders of my work with clients upset me”). This subscale specifically (rather than entire measure) was selected for purposes of reducing potential participant fatigue, and because the Intrusion subscale is highly correlated with the other measure subscales (Intrusion-Arousal r=.96; Intrusion-Avoidance r=.96 (Ting et al., 2005). Each item was rated on a 5-point scale from 1 to 5 (1=Never, 5=Very often), and higher scores corresponded to higher secondary traumatic stress. A mean composite score was used for analyses, and the final variable ranged from 1 to 5 (M=1.84, Mdn=1.60, SD=.70).

Therapist Emotional Exhaustion (Dependent Variable)

The emotional exhaustion measure is adapted from a subscale of the Organizational Social Context Questionnaire (OSC; Glisson et al., 2007) and assesses therapist fatigue and exhaustion from work (α=.93; e.g., “I feel emotionally drained from my work,” “I feel burned out from my work”). The 5-item measure is rated on a 1 (strongly disagree) to 7 (strongly agree) Likert scale, and higher scores corresponded to higher levels of emotional exhaustion. A mean composite score was used for analyses, which ranged from 1 to 7 (M=4.02, Mdn=4.00, SD=1.56).

Therapist Demographic, Professional, and Caseload Characteristics (Independent Variables)

As a part of the survey data, therapists reported their age, race/ethnicity, licensure status, theoretical orientation, education level, ability to deliver services in another language, years practiced at their current clinic, number of clients on their caseload, and the number of clients a therapist is treating with trauma as their presenting problem. The number of trauma clients on their caseload was divided by total therapist caseload to determine the percentage of trauma clients on therapists’ caseloads.

Organizational Cohesion (Independent Variable)

Therapist perceptions of organizational climate including resources supporting implementation of EBPs was measured via the Organizational Readiness for Change questionnaire (Lehman & Simpson, 2002). Specifically, the Cohesion subscale which measures group cohesion at the clinic level was used (α=.69; e.g., “Mutual trust and cooperation among therapists in your program are strong”). Therapists responded to four items on a 5-point scale (1=disagree strongly, 5=agree strongly), and an average therapist-level score was calculated. Therapist scores were then aggregated to the clinic level.

Data Analytic Plan

The current study aimed to 1) identify associations between neighborhood sociodemographic disadvantage and two therapist well-being constructs (specifically secondary traumatic stress and emotional exhaustion); and 2) examine potential clinic and therapist level explanatory factors in the associations between neighborhood sociodemographic disadvantage and therapist well-being (specifically secondary traumatic stress and emotional exhaustion).

To examine Aim 1, Stata/SE (version 15.1) was used to examine a mixed effects multilevel regression model to investigate associations between two therapist well-being constructs (specifically secondary traumatic stress and emotional exhaustion) and neighborhood sociodemographic disadvantage. While null models showed minimal variation at the neighborhood (ICC=<.01) and clinic level (ICC=.01), a three-level model was used to account conceptually for clustering effects (level 1=therapist, level 2=clinic, level 3=neighborhood). The model specified therapist at level 1 (n = 560), clinic at level 2 (n=112), and neighborhood of the clinic at level 3 (n=60). Demographic variables and professional characteristics (age, gender, race/ethnicity, education, psychotherapy orientation, licensure, number of years practiced at their clinic, number of clients, and ability to deliver services in a non-English language) were also included in the models.

To examine Aim 2, the same mixed effects multilevel regression model from Aim 1 was used with the following independent variables added to the model: therapist percentage of trauma clients on caseload and clinic organizational climate. Therapist emotional exhaustion was added as an independent variable for secondary traumatic stress (dependent variable) model and secondary traumatic stress was added as an independent variable for the emotional exhaustion (dependent variable) model.

Results

Aim 1: Identify Associations Between Neighborhood Sociodemographic Disadvantage and Two Therapist Well-Being Constructs, Specifically Therapist Secondary Traumatic Stress and Emotional Exhaustion

Two multi-level mixed models were run (see Table 2) examining correlates of therapist secondary traumatic stress and emotional exhaustion with the following covariates included: therapist age, gender, race/ethnicity, education, psychotherapy orientation (cognitive behavioral versus other), licensure, number of years practiced at their clinic, number of clients, and non-English language ability. Results showed neighborhood sociodemographic disadvantage was significantly associated with higher therapist secondary traumatic stress (B = .09, SE = .04, p < .05) but not emotional exhaustion. Essentially, upon controlling for other variables in the model, for each one unit increase in neighborhood sociodemographic disadvantage, secondary traumatic stress increased by .09 units on a five-point scale. In addition, therapist age (B = −.01, SE = .004, p < .05) was significantly associated with lower secondary traumatic stress, while therapist age (B = −.03, SE = .01, p < .01), Hispanic ethnicity (B = −.73, SE = .22, p < .01), and being licensed (B = −.31, SE = .16, p < .05) were significantly associated with lower emotional exhaustion.

Table 2.

Aim 1: Association Between Neighborhood Sociodemographic Disadvantage and Two Therapist Well-Being Constructs, Specifically Therapist Secondary Traumatic Stress and Emotional Exhaustion with Covariates Added

Secondary Traumatic Stress Emotional Exhaustion
B (SE) 95% Confidence Interval B (SE) 95% Confidence Interval
LL UL LL UL
Therapist level covariates
 Age −.01 (.004)* −.02 −.001 −.03 (.01)** −.05 −.01
 Gender (ref male) .19 (.10) −.01 .40 .24 (.22) −.20 .67
 Race/ethnicity (ref non-Hispanic White)
  Hispanic −.16 (.10) −.35 .04 −.73 (.22)** −1.16 −.30
  Other −.08 (.09) .26 .10 −.03 (.20) −.42 .35
 Education (ref master’s degree)
  Bachelor’s degree −.37 (.21) −.79 .04 −.53 (.46) −1.42 .37
  Doctorate degree .01 (.12) −.22 .24 .02 (.26) −.49 .53
 Psychotherapy orientation .06 (.07) −.06 .19 −.20 (.14) −.47 .08
 Licensed −.10 (.07) −.24 .04 −.31 (.16)* −.62 −.01
 Number of years practiced at clinic −.01 (.01) −.02 .02 −.01 (.02) −.06 .03
 Number of clients .001 (.004) −.01 01 .01 (.01) −.01 02
 Non-English language ability −.01 (.08) −.18 .15 .04 (.18) −.31 .40
Neighborhood sociodemographic disadvantage .09 (.04)* .02 .16 .01 (.08) −.16 .17
Intercept 2.09 (.21)** 1.68 2.51 5.39 (.46)** 4.49 6.30
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Note:

*

p < .05;

**

p < .01

Aim 2: Examine Potential Clinic and Therapist Level Explanatory Factors in the Associations Between Neighborhood Sociodemographic Disadvantage and Two Therapist Well-Being Constructs, Specifically Therapist Secondary Traumatic Stress and Emotional Exhaustion

Two additional multi-level mixed models were run (see Table 3) examining potential explanatory factors associated with therapist secondary traumatic stress and emotional exhaustion. Therapist percentage trauma caseload and clinic organizational climate—cohesion were added as independent variables. Therapist emotional exhaustion was also added as an independent variable in the secondary traumatic stress model, and secondary traumatic stress was added as an independent variable in the emotional exhaustion model. Results showed neighborhood sociodemographic disadvantage was significantly associated with higher therapist secondary traumatic stress (B = .08, SE = .03, p < .05) but not emotional exhaustion. Essentially, upon controlling for other variables in the model, for each one unit increase in neighborhood sociodemographic disadvantage, secondary traumatic stress increased by .08 units on a five-point scale. In addition, therapist emotional exhaustion (independent variable; B = .18, SE = .02, p < .01) was significantly associated with higher therapist secondary traumatic stress (dependent variable), and secondary traumatic stress (independent variable; B = .84, SE = .09, p < .01) was significantly associated with higher emotional exhaustion (dependent variable). Furthermore, therapist age (B = −.02, SE = .01, p < .05), Hispanic ethnicity (B = −.63, SE = .20, p < .01), and clinic organizational climate—cohesion (B = −.46, SE = .19, p < .05) were significantly associated with lower therapist emotional exhaustion.

Table 3.

Aim 2: Association Between Neighborhood Sociodemographic Disadvantage and Two Therapist Well-Being Constructs, Specifically Therapist Secondary Traumatic Stress and Emotional Exhaustion with Covariates and Explanatory Variables Added

Secondary Traumatic Stress Emotional Exhaustion
B (SE) 95% Confidence Interval B (SE) 95% Confidence Interval
LL UL LL UL
Therapist level covariates
 Age −.004 (.004) −.01 .004 −.02 (.01)* −.04 −.005
 Gender (ref male) .13 (.10) −.06 .32 .14 (.21) −.27 .55
 Race/ethnicity (ref non-Hispanic White)
  Hispanic −.01 (.10) −.20 .17 −.63 (.20)** −1.03 −.24
  Other −.07 (.09) −.23 .10 .03 (.18) −.33 .39
 Education (ref master’s degree)
  Bachelor’s degree −.25 (.20) −.63 .13 −.17 (.42) −.99 .65
  Doctorate degree −.000 (.11) −.21 .21 .02 (.24) −.44 .48
 Psychotherapy orientation .09 (.06) −.03 .21 −.20 (.13) −.46 .06
 Licensed −.05 (.07) −.18 .08 −.20 (.15) −.49 .08
 Number of years practiced at clinic −.002 (.01) −.02 .02 −.01 (.02) −.05 .03
 Number of clients −.002 (.003) −.01 .01 .01 (.01) −.01 02
 Non-English language ability −.02 (.08) −.17 .14 .10 (.17) −.23 .43
Explanatory variables
 Therapist percentage trauma caseload .15 (.12) −.09 .40 −.10 (.26) −.61 .42
 Therapist secondary traumatic stress − − − .84 (.09)** .66 1.02
 Therapist emotional exhaustion .18 (.02)** .14 .22 − − −
 Clinic organizational climate—cohesion −.06 (.09) −.23 .12 −.46 (.19)* −.84 −.08
Neighborhood sociodemographic disadvantage .08 (.03)* .01 .11 −.05 (.07) −.19 .10
Intercept 1.35 (.44)* .49 2.22 5.46 (.95)** 3.61 7.32
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Note:

*

p < .05;

**

p < .01

Discussion

We examined how neighborhood sociodemographic disadvantage is associated with the presence of two therapist well-being constructs, specifically secondary traumatic stress and emotional exhaustion, among therapists working in these clinics within the increasingly common context of a system-driven effort to increase multiple EBP implementation. This is the first study to our knowledge to examine how neighborhood sociodemographic disadvantage is linked to therapist well-being. Levels of neighborhood sociodemographic disadvantage were positively associated with therapist secondary traumatic stress. When explanatory variables were added to the model, neighborhood sociodemographic disadvantage continued to be associated with higher secondary traumatic stress but not therapist emotional exhaustion. On the flipside, levels of neighborhood sociodemographic disadvantage were not significantly associated with therapist emotional exhaustion. When explanatory variables were added to the model, neighborhood sociodemographic disadvantage continued to not be a statistically significant correlate of emotional exhaustion, but therapist secondary traumatic stress (positive coefficient) and clinic organizational climate—cohesion (negative coefficient) were significantly associated with emotional exhaustion.

To contextualize our findings, it is worth noting that there were large discrepancies in sociodemographic disadvantage among neighborhoods in Los Angeles County (e.g., median neighborhood income ranged from $15,000 to $208,000 per year). Due to sociodemographic data being collected at the neighborhood level, the full range of sociodemographic (dis)advantage within neighborhoods was likely even more expansive than represented.

This study validates that community mental health clinics are located in communities with the greatest need. Because community therapists in Los Angeles County often work with clients in neighborhoods where the population faces high levels of sociodemographic disadvantage, perhaps these therapists feel a greater sense of urgency to provide high quality mental health services for their clients—particularly if clients disclose financial stress that prevents them from receiving other types of psychosocial and medical services. Knowing that clients may have limited treatment options may pose unique stressors on the therapists, thus highlighting the importance of evaluating and supporting the well-being of community therapists working with vulnerable populations.

Higher levels of neighborhood sociodemographic disadvantage were also found to be significantly positively associated with higher therapist secondary traumatic stress. Not only are these results concerning for therapists’ well-being and these already negatively impacted neighborhoods, but the results are also concerning for EBP implementation. Since secondary traumatic stress is linked to therapist turnover, neighborhood sociodemographic disadvantage serves as a threat to EBP sustainability via secondary traumatic stress among therapists whose agencies have invested in training them in EBPs (Cieslak et al., 2013; Malkina-Pykh, 2017; Middleton & Potter, 2015). Additional research is needed to understand the mechanisms by which neighborhood sociodemographic disadvantage contributes to therapist secondary traumatic stress. One possibility is that, similar to their clients, therapists working in these neighborhoods may be more prone to experiencing a traumatic event, which increases the likelihood of therapists’ experiencing secondary traumatic stress (Baglivio et al., 2015; Killian, 2008). Another possibility is that parallel to how children in these disadvantaged neighborhoods are more likely to have an external locus of control and low self-competence, therapists working in disadvantaged neighborhoods may feel they have less control over the risk factors impacting their clients (Farver et al., 2000). In turn, this may contribute to therapists experiencing an external locus of control, which is linked to secondary traumatic stress (Killian, 2008). To mitigate the negative effects of neighborhood sociodemographic disadvantage, this study’s findings suggest that additional supports are needed for mental health systems and agencies where employed therapists are at a higher risk for secondary traumatic stress. While the current state of evidence-based research on interventions to secondary traumatic stress is in its infancy (Bercier & Maynard, 2015), there is a small but growing body of research showing that interventions that include trauma narration, elements of empirically based therapies for posttraumatic stress disorder, addressing symptoms of burnout, and destigmatizing secondary trauma at the organizational level may reduce therapist secondary traumatic stress symptoms (Penix et al., 2019; Tullberg & Boothe, 2019).

Interestingly, neighborhood sociodemographic disadvantage was not significantly associated with therapist emotional exhaustion. Such null results are comforting in that therapists working in areas of high neighborhood sociodemographic disadvantage do not appear to be at high risk for feeling burnout due to the location of the clinic in which they work. Findings imply that other variables outside of the direct neighborhood context are more relevant in understanding therapist emotional exhaustion. More specifically, therapists who are older, those who identify as Hispanic as opposed to non-Hispanic White, and those who are licensed all appear to have lower levels of emotional exhaustion, which is consistent with findings in previous literature suggesting that therapist background and professional characteristics can both serve as protective and risk factors in relation to secondary traumatic stress (Diehm et al., 2019; Hensel et al., 2015; Kanno et al., 2016). Perhaps therapist personal and professional background characteristics may serve as buffers in reducing therapist emotional exhaustion.

Since some studies suggest agencies may be able to intervene to reduce therapist secondary traumatic stress (e.g., Tullberg & Boothe, 2019), this study also examined explanatory variables associated with secondary traumatic stress and emotional exhaustion including organizational support. When, organizational climate, therapist percentage of trauma caseload, and therapist emotional exhaustion were added as independent variables to the model of neighborhood sociodemographic disadvantage (independent variable) and secondary traumatic stress (dependent variable), therapist emotional exhaustion in addition to neighborhood sociodemographic disadvantage were significantly associated with experiencing more secondary traumatic stress. These results confirm previous findings that emotional exhaustion is linked to secondary traumatic stress (Cieslak et al., 2014; Malkina-Pykh, 2017). Due to the larger coefficient observed for emotional exhaustion in the secondary traumatic stress model, perhaps emotional exhaustion is more relevant in understanding correlates of secondary traumatic stress than broader neighborhood context. With (non)supportive in-clinic interactions and one’s caseload, two variables that are highly associated with therapist emotional exhaustion (Choi, 2011; Penix et al., 2019; Quinn et al., 2018), often being directly observable to a therapist, it follows that emotional exhaustion would have a larger correlation with secondary traumatic stress than the external neighborhood context. Additional research is needed however in identifying additional therapist- and clinic-level variables that may mediate or moderate the relationship between neighborhood sociodemographic disadvantage and therapist secondary traumatic stress.

When, organizational climate, therapist percentage of trauma caseload, and therapist secondary traumatic stress were added as independent variables to the model of neighborhood sociodemographic disadvantage and emotional exhaustion, therapist secondary traumatic stress in addition to cohesion in the clinic organizational climate were associated with emotional exhaustion. These findings highlight the relevance of the organizational climate rather than clinic neighborhood in understanding correlates of emotional exhaustion. Therapists working in community clinics with low levels or organizational cohesion appear to have high emotional exhaustion, which is unsurprising given consistent findings throughout the scientific literature (Choi, 2011; Penix et al., 2019; Quinn et al., 2018). Therapist secondary traumatic stress was also significantly associated with emotional exhaustion, which further supports linkages between secondary traumatic stress and emotional exhaustion (Cieslak et al., 2014; Malkina-Pykh, 2017).

Overall, the current study is the first to examine neighborhood-level determinants of therapist well-being, specifically secondary traumatic stress and emotional exhaustion within a large-scale, system-wide implementation effort of EBPs and their sustainment. Previously, research has primarily focused on the relationship between neighborhood characteristics as risk factors of poor youth mental health (Roosa et al., 2010; White et al., 2016). The current study is novel in that we extend previous work to examine how the environmental context of employment may warrant additional supports for the youth mental health workforce serving families experiencing trauma.

Despite study strengths, the findings must be interpreted within the context of several limitations. First, the neighborhood-level data corresponds to the location of clinics but does not necessarily correspond to where the client receives services, as they may also receive home-based care. Given that community clinics usually offer services within specific service planning areas, it is highly likely that the clients served do live in the clinic’s geographical area, but unfortunately, we were unable to confirm. Second, in developing the neighborhood cumulative risk index, we were limited by the demographic data available to devise the specific neighborhood sociodemographic disadvantage components. Importantly, we were unable to calculate average number of families living below the poverty line as we did not know the number of individuals who were supported by the reported household income. We were also unable to examine other important demographic information associated with sociodemographic disadvantage including the percentage of vacant housing and availability of adequate plumbing. Furthermore, we were also limited to demographic information collected as part of the 2000 census rather than the year therapist data were collected in 2019, and this mismatch between timepoints of data collection should be noted. Census data at the county level, however, indicates the demographic variables of interest remained relatively stable from 2000 to 2020 (e.g., Brookings Institution Center on Urban and Metropolitan Policy, 2001; United States Census Bureau, 2002, 2022a, 2022b). Third, based on data collected from the therapist survey, examining therapist previous experiences of their own traumatic events or coping skills in response to work stress could not be examined nor controlled for in study aims. It is possible that therapists’ personal histories or minimal use of coping skills may have contributed to an elevated level of secondary traumatic stress. Forth, while the therapists selected their clinic where they work at the time of the therapist survey in 2019, due to data availability, the clinic addresses were taken from administrative claims data corresponding to 2017–2018. It is unlikely that a sizeable number of clinics moved location to a different neighborhood from 2018 to 2019, however this mismatch in years of data collection should be noted. Fifth, because study staff members were unable to assess if a given therapist email address was active or not, the research team was unable to assess comparisons between therapists who did and did not respond to the study survey. Therefore, non-response bias may have applied to the current therapist survey data, but research staff were unable to assess the extent to which non-response bias may or may not have applied. Sixth, though this study found a significant positive association between secondary traumatic stress and neighborhood sociodemographic disadvantage, due to statistical limitations with calculating effect sizes in multilevel models (Garson, 2012; Hössjer, 2008; Snijders & Bosker, 2012), the authors were not able to statistically quantify the real-world importance of these findings.

With these limitations, the current findings provide guidance on future research. Importantly, more work on examining the influence of neighborhood context and therapist secondary traumatic stress is needed to inform prevention or support strategies, such as evidence-based interventions to reduce secondary traumatic stress at the clinic level. Furthermore, this study did not examine connections between where therapists live and their secondary traumatic stress. Given that therapist personal factors, including financial stress, appear connected to therapist well-being (Last et al., 2022), it would be beneficial for future studies to examine if therapist home location moderates the relation between neighborhood sociodemographic stress and therapist secondary traumatic stress. In addition, as the rollout of trauma-focused EBPs continues, intervention developers may consider adding content into their trainings to raise therapist awareness of their own well-being when delivering EBPs to clients with trauma as their presenting problems. Supervisors may be able to help guide their supervisees to self-care practices to reduce symptoms of secondary traumatic stress. Ultimately, there is much more work to be done to inform appropriate and feasible prevention and support strategies for community therapists, and mixed-methods studies soliciting therapists’ ideas would be of great importance.

Conclusion

In conclusion, findings delineate a clear need in implementing resources and staff supports that will protect community therapists from secondary traumatic stress in particular for therapists delivering trauma focused EBPs to clients in neighborhoods with high sociodemographic disadvantage. Unpacking how leaders, clinics, and mental health systems can pivot to better support therapists who work in high stress environments seems essential for the longevity of the community mental health workforce. The current study brings increased attention to the needs of community therapists working and delivering EBPs in disadvantaged environments. To bolster the success of large scale EBP implementation efforts, supporting both the families served and the community providers is essential.

Supplementary Material

Supplemental Table 1

Acknowledgments

Funding for this research project, including the study design and data collection, analysis and interpretation of data, was supported by NIMH Grant R01MH100134 to Anna Shan-Lai Lau and Lauren Brookman-Frazee, NIMH Grant K23MH123067 to Teresa A. Lind, and NIDA Grant T32DA039772 to Nancy A. Gonzales and Cady Berkel.

Appendix 1

Neighborhood Name Neighborhood Sociodemographic Disadvantage Score
Altadena 0
Avocado Heights 0
Baldwin Hills/Crenshaw 1
Baldwin Park 0
Bell Gardens 2
Boyle Heights 2
Burbank 0
Carson 0
Cerritos 0
Charter Oak 0
Cheviot Hills 0
Commerce 1
Compton 0
Covina 0
Culver City 0
Downtown 1
Duarte 0
East Hollywood 2
East Los Angeles 1
Echo Park 0
El Monte 0
Florence-Firestone 2
Gardena 0
Glendale 0
Granada Hills 0
Green Meadows 3
Harbor Gateway 0
Highland Park 0
Historic South-Central 4
Hollywood 2
Inglewood 1
Irwindale 0
Lancaster 0
Lincoln Heights 1
Long Beach 0
Lynwood 1
Mid-Wilshire 0
North Hills 0
North Hollywood 0
Northridge 0
Palmdale 0
Pasadena 0
Pico-Union 3
San Fernando 0
San Pedro 0
Santa Clarita 0
Santa Monica 0
South Pasadena 0
Sylmar 0
Torrance 0
Van Nuys 0
Vermont Knolls 3
Vermont Vista 2
West Carson 0
West Covina 0
West Hills 0
West Los Angeles 0
Westlake 3
Westwood 0
Whittier 0
Open in a new tab

Footnotes

The Author(s) declare that there is no conflict of interest.

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