Why do Stayers Stay? Perceptions of White and Black Long-Term Employees in a Community Mental Health Center.

Previous research has focused on factors influencing turnover of employees in the mental health workforce, yet little research has explored reasons why employees stay. To facilitate retaining a diverse mental health workforce, the current study aimed to elucidate factors that contributed to employees' tenure at a community mental health center (CHMC) as well as compare these perceptions between Black and White employees. Long-term employees (7 years or more) from one urban CMHC (n = 22) completed semi-structured stayer interviews. Using emergent thematic analysis, stayer interviews revealed four major themes for why they have stayed at the organization for 7 years or more: (1) work as a calling, (2) supportive relationships, (3) opportunities for growth or meaningful contribution, and (4) organization mission's alignment with personal attributes or values. Comparison between Black and White stayer narratives revealed differences in their perceptions with work as a calling and opportunities for growth and meaningful contribution. Guided by themes derived from stayer interviews, the current study discusses theoretical (e.g., job embeddedness theory, theory of racialized organizations, self-determination theory) and practical implications (e.g., supporting job autonomy, Black voices in leadership) in an effort to improve employee retention and address structural racism within a mental health organization.

Prevalence and predictors of medication for opioid use disorder among reproductive-aged women.

Background: Women of reproductive age would benefit from treatment of opioid use disorder (OUD) prior to pregnancy to improve maternal and infant outcomes. In this study, we aimed to identify the prevalence of medication for OUD (MOUD) and characterize correlates of MOUD receipt among 12-49-year-old women with OUD seeking treatment in publicly funded substance use disorder treatment programs at the time of their first treatment episode. Methods: This cross-sectional study explores the demographic and clinical characteristics of women of reproductive age with OUD receiving publicly funded substance use treatment services. We used data from the concatenated 2015-2021 Treatment Episode Data Set-Admissions (TEDS-A), which documents demographic and clinical characteristics of patient admissions to publicly funded substance use treatment services in the United States. Results: In the sample of females aged 12-49 with no prior treatment admissions and primary OUD (n=325,512), 40.53% received MOUD (n=131,930), including 39.40% of non-pregnant women (n=115,315) and 52.79% of pregnant women (n=8423). Pregnant women had significantly higher odds of receiving MOUD (aOR = 2.42, 95%CI: 2.30, 2.54) compared to non-pregnant women. Non-white race, treatment setting, and treatment self-referral were also associated with higher levels of MOUD. Conclusions: We identified a significant unmet need among both pregnant and non-pregnant women with OUD seeking care in publicly funded treatment clinics. While women who are pregnant are significantly more likely to receive evidence-based treatment with MOUD, still 47.21% of pregnant women did not receive MOUD. All reproductive-aged women with OUD should be offered evidence-based treatment options, including MOUD.

A machine learning approach using migration-related cultural stress to classify depression and post-traumatic stress disorder among hurricane survivors.

BACKGROUND: Data science approaches have increasingly been used in behavioral health research and may be useful for addressing social factors contributing to disparities in health status. This study evaluated the importance of cultural stress-related factors in classifying depression and post-traumatic stress disorder (PTSD) among adult survivors (N = 319) of Hurricane Maria who migrated from Puerto Rico to the United States mainland. METHODS: We evaluated the performance of random forests (RF) and logistic regression (LR) for classifying PTSD and depression. Models included demographic, hurricane exposure, and migration-related cultural stress variables. We inspected area under the receiver operating characteristic curve (AUC), accuracy, balanced accuracy, F1 score, precision, recall, and specificity. RESULTS: Negative context of reception and language-related stressors were moderately important for accurately classifying depression and PTSD. For classifying depression, RF showed higher accuracy, balanced accuracy, specificity, precision, and F1. For classifying PTSD, RF showed higher accuracy, specificity, precision, and F1. LIMITATIONS: A more thorough classification model would also include biomarkers (e.g., of allostatic load), family, community, or neighborhood-level attributes. Findings may not generalize to other groups who have experienced crisis-related migration. CONCLUSIONS: Findings underscore the importance of culturally and linguistically appropriate and trauma-informed clinical services for recent migrants. Use of assessments to identify pre-migration and post-migration stressors could inform clinical practice with migrants presenting with behavioral health-related difficulties.

Firearm injury among people experiencing homelessness: Cross-sectional evidence from a national survey of United States emergency departments.

Objectives: Persons experiencing homelessness (PEH) are at high risk for violent victimization. This study leverages unique data from a national study in the United States of America to provide estimates of non-fatal firearm injury among PEH and to describe the contexts related to injury, such as substance use, intent of the injury, and precipitating interpersonal factors. Study design: Cross-sectional. Methods: Data from the 1993-2020 National Electronic Injury Surveillance System-Firearm Injury Surveillance Study (NEISS-FISS) were used to describe the context and characteristics of non-fatal firearm injury among PEH aged 16 years or older. Homeless status and substance use data were extracted from a de-identified narrative field. Estimates were weighted to account for the NEISS-FISS complex sampling design. Results: Probable homelessness was identified in 0.10% of cases (n = 3,225). Substance use was documented in 22.73% of cases. Assault comprised 82.64% of injuries. Patients were mostly male (81.38%). Missing data were common on contextual variables: verbal argument (64.62%), physical fight (54.48%) or other criminal activity (62.33%). Conclusions: Assault is a leading cause of non-fatal firearm injury for PEH and is greater than rates of assault in non-fatal firearm injuries in the general population. Substance use was documented in nearly one quarter of patients, although this is less than expected given prior evidence. Reliance on narrative fields for key variables likely underestimates rates of PEH and substance use.

The Relationship Between Serious Mental Illness and Criminal Offending in Persons Experiencing Homelessness: The Role of Substance Use Disorder.

Individuals who live with mental illness are encumbered by related risk factors that increase the probability of legal involvement. The goal was to determine how homelessness and substance use disorder are intervening factors in the relationship between symptoms of serious mental illness (SMI) and criminal offending. A sample of 210 chronically homeless adults receiving SAMHSA-funded outreach and psychiatric rehabilitation services between 2014 and 2016 was recruited in a study of interventions to address housing in homeless persons with a SMI. Participants were interviewed and data collected were analyzed using structural equation modeling. Statistical analysis showed that homelessness severity mediated the relationship between SMI symptom severity and criminal offenses committed in the past 30 days in participants with a substance use disorder but not in those with no substance use diagnosis. Results show that homelessness and substance use are important to address to possibly alter trajectories for criminal justice involvement.

Prevalence and correlates of knife-related victimization: Insights from the 2019 Nationwide Emergency Department Sample.

Few studies have explored the incidence and general trends in knife-related victimizations in the United States (US), especially in settings where preventive interventions can potentially be initiated such as emergency departments (EDs). The goal of the present investigation was to provide an empirical portrait of the psychosocial and behavioral health characteristics of patients assaulted by sharp objects, particularly knives, as revealed in EDs in the US, as less research has focused on knife victimization in the US than internationally. This study uses data from the 2019 Nationwide Emergency Department Sample (NEDS), which is part of the Healthcare Cost and Utilization Project distributed by the Agency for Healthcare Research and Quality. Findings revealed that demographically males (especially those aged 18-25), those in poverty, and members of racially minoritized groups were more likely to be present with knife-related assault. Key factors increasing the odds of knife-related victimization treated in EDs were homelessness, legal involvement, and substance use, particularly alcohol and stimulant use disorder. Somewhat surprisingly, mental health diagnosis was not associated with increased knife-related victimization. Although EDs are critical to treating knife-related victimization, they are also potentially key points to launch prevention for high-risk individuals to reduce subsequent violence stemming from escalation of interpersonal disputes.

Machine Learning with Human Resources Data: Predicting Turnover among Community Mental Health Center Employees.

BACKGROUND: Human resources (HR) departments collect extensive employee data that can be useful for predicting turnover. Yet, these data are not often used to address turnover due to the complex nature of recorded data forms. AIMS OF THE STUDY: The goal of the current study was to predict community mental health center employees' turnover by applying machine learning (ML) methods to HR data and to evaluate the feasibility of the ML approaches. METHODS: Historical HR data were obtained from two community mental health centers, and ML approaches with random forest and lasso regression as training models were applied. RESULTS: The results suggested a good level of predictive accuracy for turnover, particularly with the random forest model (e.g., Area Under the Curve was above .8) compared to the lasso regression model overall. The study also found that the ML methods could identify several important predictors (e.g., past work years, wage, work hours, age, job position, training hours, and marital status) for turnover using historical HR data. The HR data extraction processes for ML applications were also evaluated as feasible. DISCUSSION: The current study confirmed the feasibility of ML approaches for predicting individual employees' turnover probabilities by using HR data the organizations had already collected in their routine organizational management practice. The developed approaches can be used to identify employees who are at high risk for turnover. Because our primary purpose was to apply ML methods to estimate an individual employee's turnover probability given their available HR data (rather than determining generalizable predictors at the wider population level), our findings are limited or restricted to the specific organizations under the study. As ML applications are accumulated across organizations, it may be expected that some findings might be more generalizable across different organizations while others may be more organization-specific (idiographic). IMPLICATIONS FOR HEALTH CARE PROVISION AND USE: The organization-specific findings can be useful for the organization's HR and leadership to evaluate and address turnover in their specific organizational contexts. Preventing extensive turnover has been a significant priority for many mental health organizations to maintain the quality of services for clients. IMPLICATIONS FOR HEALTH POLICIES: The generalizable findings may contribute to broader policy and workforce development efforts. IMPLICATIONS FOR FURTHER RESEARCH: As our continuing research effort, it is important to study how the ML methods and outputs can be meaningfully utilized in routine management and leadership practice settings in mental health (including how to develop organization-tailored intervention strategies to support and retain employees) beyond identifying high turnover risk individuals. Such organization-based intervention strategies with ML applications can be accumulated and shared by organizations, which will facilitate the evidence-based learning communities to address turnover. This, in turn, may enhance the quality of care we can offer to clients. The continuing efforts will provide new insights and avenues to address data-driven, evidence-based turnover prediction and prevention strategies using HR data that are often under-utilized.

Correlates of Homelessness Among Adults with Personality Disorder.

Although personality disorders (PDs) are more common among persons experiencing homelessness than the general population, few studies have investigated the risk of experiencing homelessness among persons with PDs. This study seeks to identify the demographic, socioeconomic, and behavioral health correlates of past-year homelessness among persons with antisocial, borderline, and schizotypal PDs. Nationally representative data of the civilian, noninstitutionalized population of the United States was used to identify correlates of homelessness. Descriptive statistics and bivariate associations between variables and homeless status were summarized prior to conducting several multivariate logistic regression models to identify correlates of homelessness. Main findings revealed positive associations between poverty, relationship dysfunction, and lifetime suicide attempt with homelessness. In the antisocial PD (ASPD) and borderline PD (BPD) models, comorbid BPD and ASPD, respectively, were associated with higher odds of past-year homelessness. Findings underscore the importance of poverty, interpersonal difficulties, and behavioral health comorbidities on homelessness among persons with ASPD, BPD, and schizotypal PD. Strategies to promote economic security, stable relationships, and interpersonal functioning may buffer against the effects of economic volatility and other systemic factors that could contribute to homelessness and persons with PD.

ICD-10-CM codes infrequently used to document human trafficking in 2019 Nationwide Emergency Department Sample.

INTRODUCTION: People who experience human trafficking (HT) visit emergency departments (ED). The International Classification of Diseases, Clinical Modification (ICD-10-CM) introduced codes to document HT in June 2018. The aim of this study is to identify characteristics of ED patients who experienced forced labor or sexual exploitation as a documented external cause of morbidity in US visits. METHODS: Nationally representative surveillance based on patient visits to 989 hospital-owned EDs in the Nationwide Emergency Department Sample in 2019 became available in 2021. Eight ICD-10-CM codes to classify HT as an external cause of morbidity were combined into one HT variable for analysis in 2021-2022. RESULTS: A weighted count of 517 of 33.1 million ED visits (0.0016%) documented HT as an external cause of morbidity. Of them, sexual exploitation (71.6%) was documented more frequently than labor exploitation (28.4%). Most HT-related codes were visits by females (87.3%) from large metropolitan areas, and identified as white. Approximately 40% of visits were from ZIP codes with a median household income less than $48,000 annually. Relative to all other ED visits, patients with HT as an external cause of morbidity had higher odds of being female (OR = 6.54, 95% CI:3.59, 11.92) and being a minor (OR = 1.76, 95% CI:1.02, 3.04). CONCLUSION: HT was rarely documented as an external cause of morbidity in 989 hospitals' ED visits from a nationally representative sample in 2019. Documentation of recently added HT ICD-10-CM codes does not appear to have been implemented sufficiently to yield an unbiased representation of those who experienced HT and presented in the ED. Efforts to enhance the utility of ICD-10-CM HT codes for surveillance and documentation must first address ED personnel training on identification and response to HT. In doing so, ED personnel also need to address ethical concerns (e.g. stigma, confidentiality, risk of patient harm) and allow for informed consent among trafficked patients in order to be scaled up responsibly.

A pilot study assessing client understanding and use of fentanyl test strips for harm reduction.

BACKGROUND: The Centers for Disease Control and Prevention reported a sharp increase in overdose deaths from illicitly manufactured fentanyls (IMFs) occurring between 2020 and 2021 in the United States. Approximately 4 in 10 drug overdose deaths involved opioids and stimulants. Fentanyl test strips (FTS), used to test drug product before use, can be a powerful harm reduction tool to promote safer use behaviors. OBJECTIVES: This pilot study assessed treatment-seeking service users' knowledge and understanding of IMFs and motivation to use FTS to prevent overdose. METHODS: Clients receiving residential-based or office-based substance use treatment services were recruited from a community mental health center in the midwestern United States to complete a harm reduction-focused survey. Eligibility criteria included clients aged 18-89 years with self-reported drug use in the past year. A 20-question survey was administered verbally, both in person and via phone to assess knowledge of IMFs and FTS. RESULTS: Of respondents (N = 30), 80% agreed that IMFs cause more overdoses than heroin. Most (73%) indicated concern about a friend overdosing owing to IMFs, and half (53%) expressed concern about personal risk of overdose. Most (73%) would like to be able to detect whether there is fentanyl in their drug before use, but only 17% indicated that they feel confident in their ability to use FTS. CONCLUSION: Many respondents who were receiving services for past-year substance use lacked understanding of how to use FTS for harm reduction. Clients who primarily use nonopioid (stimulant) drug products are at even greater risk of IMF overdose and would benefit the most from increased access and education surrounding the use of FTS. Our health care system must rapidly continue to explore and expand on overdose prevention efforts, including access to FTS, given that urgent action is needed to reduce the continued rise in overdose deaths in the United States.

Correlates of cannabis use disorder in the United States: A comparison of logistic regression, classification trees, and random forests.

Although several recent studies have examined psychosocial and demographic correlates of cannabis use disorder (CUD) in adults, few, if any, recent studies have evaluated the performance of machine learning methods relative to standard logistic regression for identifying correlates of CUD. The present study used pooled data from the 2015-2018 National Survey on Drug Use and Health to evaluate psychosocial and demographic correlates of CUD in adults. In addition, we compared the performance of logistic regression, classification trees, and random forest methods in classifying CUD. When comparing the performance of each method on the test data set, classification trees (AUC = 0.84, 95%CI: 0.82, 0.85) and random forest (AUC = 0.83, 95%CI: 0.82, 8.05) performed similarly and superior to logistic regression (AUC = 0.77, 95%CI: 0.74, 0.79). Results of the random forests reveal that marital status, risk propensity, age, and cocaine dependence variables contributed most to node purity, whereas model accuracy would decrease significantly if county type, income, race, and education variables were excluded from the model. One possible approach to improving the efficiency, interpretability, and clinical insights of CUD correlates is the employment of machine learning techniques.

Associations between Allostatic Load and Posttraumatic Stress Disorder: A Scoping Review.

Traumatic events may lead to the development of PTSD or PTSD symptomology that can negatively impact health outcomes. Allostatic load, which represents the cumulative biological wear and tear of exposure to stress, can be employed to better understand the physiological etiology of PTSD. A scoping review, informed by the PRISMA Extension for Scoping Reviews, was completed to identify published and unpublished empirical studies that explored the relationship of allostatic load and PTSD. Eight databases were searched yielding four studies, with the results summarized narratively and in tabular form. Findings were mixed as to the association between allostatic load and PTSD. Allostatic load was positively associated with PTSD diagnosis; however, studies measuring only PTSD symptomatology found no relationship. Few PTSD researchers have tested the relationship of a comprehensive measure of allostatic load to PTSD. Among those who have, studies show that allostatic load can be a beneficial tool and applicable theoretical framework for understanding the biological changes associated with PTSD. Future research should focus on standardizing measures of both allostatic load and PTSD and identifying potential confounders. A better understanding of the physiological changes associated with PTSD could lead to more effective prevention and treatment efforts.

Happiness, well-being, and recovery: Experiences of adults receiving psychiatric rehabilitation services.

OBJECTIVES: Although recovery for people with serious mental illness (SMI) has received considerable attention, there is a research gap on the related topics of well-being and happiness-subjects that have been widely studied in the general population. The objective of this study was to explore the experiences of happiness, well-being, and recovery from the perspectives of persons with SMI, and to examine similarities and differences among these constructs. METHOD: This qualitative study used semistructured interviews with a purposive sample of 30 adults with SMI to explore experiences of happiness, well-being, and recovery. Reflexive thematic analysis was applied to generate themes related to each concept. RESULTS: Happiness was facilitated through relationships with natural and professional supports, engaging in meaningful activities that conferred a sense of accomplishment, using coping skills to reduce distress, engaging in recreational activities, and meeting basic material needs. Well-being was promoted through self-care activities and enhanced through relationships with professional supports. However, nearly one-fourth of participants expressed uncertainty about what well-being meant to them. Recovery was characterized as a process of overcoming illness or adversity through which personal control over symptoms and stressors is increased; recovery is also facilitated by supportive professional relationships. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Most factors relating to the happiness of people with SMI are also factors that have been identified in theory and research as facilitating the happiness of the general population. Findings suggest that happiness should be considered an important new area of research, policy, and practice in the fields of psychiatric rehabilitation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Binge drinking in early adulthood: A machine learning approach.

Binge drinking among young adults (18-25) has been recognized as a public health concern. Considerable variation among drinking behaviors have been found among this group. Several statistical methods are available to identify theoretically and empirically meaningful correlates of binge drinking. The present study evaluated three methods for identifying correlates of binge drinking, comparing logistic regression to two machine learning methods-classification tress and random forests. While each model identified similar correlates of binge drinking-such as propensity for engaging in risky behaviors, marijuana dependence, cocaine dependence, identifying as non-Hispanic white, and higher education-the AUC analysis showed that the random forest analysis more accurately classified positive cases of binge drinking. Random forests modelling of psychosocial data is a feasible approach for identifying correlates of binge drinking behaviors among young adults. Clinical implications are discussed related to screening for binge drinking in behavioral health organizations.

Models of mental health recovery: An overview of systematic reviews and qualitative meta-syntheses.

OBJECTIVE: This study systematically reviews the systematic review (SR) evidence on mental health recovery from the perspective of adults with mental illness. METHODS: Web of Science, ProQuest Dissertations & Theses, CINAHL, PubMed, PsycINFO, Scopus, and the libraries of the Cochrane Collaboration, Campbell Collaboration, and Joanna Briggs Institute were searched to identify eligible SRs including qualitative primary research. Two reviewers independently conducted data extraction and quality assessment. Overlap of primary studies was calculated. A framework for recovery was generated using reflexive thematic analysis. RESULTS: An ecological model of recovery that included elements of psychological well-being was generated from 25 studies bridging personal/clinical, individual/social, and process/outcome conceptualizations of recovery. The first theme was a definition of recovery as a transformation from a negative identity state marked by despair, brokenness, and helplessness to a positive state of psychological well-being. This transformation was contingent upon four additional themes that included: (a) social and environmental conditions supporting access to basic resources and safety; (b) development of a sense of autonomy and personal responsibility; (c) roles and relationships that facilitated the personal experience of belonging and meaning; and (d) enlightenment defined as acceptance of the illness as a part of oneself and insight into how to promote well-being. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Professionals working in recovery-oriented systems must consider how the subjective self-representations of persons with mental illness and objective social relationships interact to impact recovery. We close by discussing interventions that may promote the personal and social conditions of recovery. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The well-being and perspectives of community-based behavioral health staff during the COVID-19 pandemic.

With high levels of burnout, turnover, and secondary traumatic stress, the well-being of the behavioral health workforce was an area of concern prior to the coronavirus disease 2019 (COVID-19) pandemic. How the COVID-19 crisis has impacted social workers, psychologists, psychiatrists, and other behavioral health professionals is unclear but should be examined. A brief survey evaluated the impact of the pandemic on the well-being of 168 behavioral health clinical and administrative staff serving in an urban behavioral health center in the United States. Staff experienced several personal and organizational-related challenges related to work-life balance, emotional distress, and organizational communication. Nevertheless, staff found an abundance of positive experiences when engaging with clients. Supportive, positive feedback and statements of appreciation from clients, colleagues, and supervisors helped staff to feel at their best. The well-being of behavioral health staff may be facilitated by consistent and supportive communication at the team, supervisory, and organizational levels and by involving staff in planning agency policy.

Direct and Indirect Associations Between Loneliness and Thoughts of Self-Harm Among a Clinical Sample of Older Adults With Serious Mental Illness.

This cross-sectional study examines the relationships of loneliness and depressive symptoms to thoughts of self-harm among a clinical sample (n = 150) of older adults (M = 58.42 years, SD = 5.86 years; male, 55.3%; African American, 61.3%) with serious mental illness (SMI) receiving publicly funded, community-based psychiatric rehabilitation services. Participants completed the De Jong Gierveld Loneliness Scale, Multidimensional Scale of Perceived Social Support, Patient Health Questionnaire 9, and Geriatric Depression Scale-Short Form. Mediation analyses tested the association of loneliness with thoughts of self-harm through depressive symptoms and were adjusted for social support and demographic variables. The direct association of loneliness with thoughts of self-harm was mediated by depressive symptoms; indirect associations of overall and emotional loneliness to thoughts of self-harm were significant. Findings suggest the need for clinicians to reduce feelings of loneliness among older adults with SMI as a means of partially ameliorating depressive symptoms and thoughts of self-harm.