Browsing by Author "Garabrant, Jennifer"

Now showing 1 - 6 of 6
  • Thumbnail Image
    Item
    eHealth Use on Acute Inpatient Mental Health Units: Implementation Processes, Common Practices, and Barriers to Use
    (Springer Nature, 2023) Bass, Emily; Garabrant, Jennifer; Salyers, Michelle P.; Patterson, Scott; Iwamasa, Gayle Y.; McGuire, Alan B.; Psychology, School of Science
    Information technology to promote health (eHealth) is an important and growing area of mental healthcare, yet little is known about the use of patient-facing eHealth in psychiatric inpatient settings. This quality improvement project examined the current practices, barriers, implementation processes, and contextual factors affecting eHealth use across multiple Veteran Health Administration (VHA) acute mental health inpatient units. Staff from units serving both voluntary and involuntary patients (n = 49 from 37 unique sites) completed surveys regarding current, desired, and barriers to use of Veteran-facing eHealth technologies. Two subsets of respondents were then interviewed (high success sites in eHealth use, n = 6; low success sites, n = 4) to better understand the context of their eHealth use. Survey responses indicated that 20% or less of Veterans were using any type of eHealth technology while inpatient. Tablets and video chat were the most desired overall and most successfully used eHealth technologies. However, many sites noted difficulty implementing these technologies (e.g., limited Wi-Fi access). Qualitative analysis of interviews revealed differences in risk/benefit analysis and implementation support between high and low success eHealth sites. Despite desired use, patient-facing eHealth technology is not regularly implemented on inpatient units due to multiple barriers (e.g., limited staffing, infrastructure needs). Successful implementation of patient-facing eHealth may require an internal champion, guidance from external supports with experience in successful eHealth use, workload balance for staff, and an overall perspective shift in the benefits to eHealth technology versus the risks.
  • Thumbnail Image
    Item
    Machine Learning with Human Resources Data: Predicting Turnover among Community Mental Health Center Employees
    (International Center of Mental Health Policy and Economics, 2023) Fukui, Sadaaki; Wu, Wei; Greenfield, Jaime; Salyers, Michelle P.; Morse, Gary; Garabrant, Jennifer; Bass, Emily; Kyere, Eric; Dell, Nathaniel; School of Social Work
    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.
  • Thumbnail Image
    Item
    Recovery-oriented Acute Inpatient Mental Health Care: Operationalization and Measurement
    (American Psychological Association, 2021) McGuire, Alan B.; Kukla, Marina; Rollins, Angela L.; Garabrant, Jennifer; Henry, Nancy; Eliacin, Johanne; Myers, Laura J.; Flanagan, Mindy E.; Hunt, Marcia G.; Iwamasa, Gayle Y.; Bauer, Sarah M.; Carter, Jessica L.; Salyers, Michelle P.; Psychology, School of Science
    Objective: The current article describes efforts to develop and test a measure of recovery-oriented inpatient care. Method: The Recovery-oriented Acute INpatient (RAIN) scale was based on prior literature and current Veterans Health Administration (VHA) policy and resources and further revised based on data collection from 34 VHA acute inpatient units. Results: A final scale of 23, behaviorally anchored items demonstrated a four-factor structure including the following factors: inpatient treatment planning, outpatient treatment planning, group programming, and milieu. While several items require additional revision to address psychometric concerns, the scale demonstrated adequate model fit and was consistent with prior literature on recovery-oriented inpatient care. Conclusions and Implementations for Practice: The RAIN scale represents an important tool for future implementation and empirical study of recovery-oriented inpatient care.
  • Thumbnail Image
    Item
    Recovery-oriented inpatient mental health care and readmission
    (American Psychological Association, 2022) McGuire, Alan B.; Flanagan, Mindy E.; Myers, Laura J.; Kukla, Marina; Rollins, Angela L.; Garabrant, Jennifer; Henry, Nancy; Eliacin, Johanne; Hunt, Marcia G.; Iwamasa, Gayle Y.; Carter, Jessica L.; Salyers, Michelle P.; Psychology, School of Science
    Objective: This article examines the relationship between inpatient mental health units' adherence to recovery-oriented care and 30-day patient readmission. Method: The sample included patients admitted to one of 34 Veterans Health Administration inpatient mental health units. Recovery-oriented care was assessed using interviews and site visits. Patient characteristics and readmission data were derived from administrative data. Findings: Overall recovery orientation was not associated with readmission. Exploratory analyses found higher scores on a subsample of items pertaining to inpatient therapeutic programming were associated with lower patient readmissions. Additionally, patients with more prior service use and substance abuse or personality disorders were more likely to be readmitted. Conclusions and implications for practice: A growing body of literature supports the association between psychotherapeutic services in inpatient units and better patient outcomes. However, further research is needed to examine this association. More work is needed to develop appropriate psychotherapy services for the inpatient setting and support their implementation.
  • Thumbnail Image
    Item
    Using Exit Surveys to Elicit Turnover Reasons among Behavioral Health Employees for Organizational Interventions
    (APA, 2025) Fukui, Sadaaki; Garabrant, Jennifer; Greenfield, Jaime; Salyers, Michelle P.; Morse, Gary; Bass, Emily; School of Social Work
    Objective: The current study explored turnover reasons via exit surveys for organizational interventions. Methods: The exit surveys were conducted at a community behavioral health organization for over a year, and the open-ended question responses on turnover reasons were analyzed. Results: Thirty-five exit surveys were returned (58% response rate). Five major turnover themes were identified: struggles in current job roles, negative experiences with upper management and senior colleagues, quality of care concerns, no foreseeable future, and personal/family reasons. Conclusions and Implications for Practice: Exit surveys are a useful approach to identify turnover reasons for organizational interventions. The findings provide insights into contextualized strategies for retaining the behavioral health workforce.
  • Thumbnail Image
    Item
    Why do Stayers Stay? Perceptions of White and Black Long-Term Employees in a Community Mental Health Center
    (Springer, 2024-06) Bass, Emily; Salyers, Michelle P.; Hall, Ashton; Garabrant, Jennifer; Morse, Gary; Kyere, Eric; Dell, Nathaniel; Greenfield, Jaime; Fukui, Sadaaki; School of Social Work
    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.