Browsing by Author "Bass, Emily"

Now showing 1 - 6 of 6
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    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.
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    Implementation and staff understanding of shared decision-making in the context of recovery-oriented care across US Veterans Health Administration (VHA) inpatient mental healthcare units: a mixed-methods evaluation
    (BMJ, 2022-05-30) Eliacin, Johanne; Carter, Jessica; Bass, Emily; Flanagan, Mindy; Salyers, Michelle P.; McGuire, Alan; Psychiatry, School of Medicine
    Objectives: To examine the understanding and practice of shared decision-making (SDM) within the context of recovery-oriented care across Veterans Health Administration (VHA) inpatient mental healthcare units. Design: VHA inpatient mental health units were scored on the Recovery-Oriented Acute Inpatient Scale (RAIN). Scores on the RAIN item for medication SDM were used to rank each site from lowest to highest. The top 7 and bottom 8 sites (n=15) were selected for additional analyses using a mixed-methods approach, involving qualitative interviews, observation notes and quantitative data. Setting: 34 VHA inpatient mental health units located in every geographical region of the USA. Participants: 55 treatment team members. Results: Our results identified an overarching theme of 'power-sharing' that describes participants' conceptualisation and practice of medication decision-making. Three levels of power sharing emerged from both interview and observational data: (1) No power sharing: patients are excluded from treatment decisions; (2) Limited power sharing: patients are informed of treatment decisions but have limited influence on the decision-making process; and (3) Shared-power: patients and providers work collaboratively and contribute to medication decisions. Comparing interview to observational data, only observational data indicating those themes differentiate top from bottom scoring sites on the RAIN SDM item scores. All but one top scoring sites indicated shared power medication decision processes, whereas bottom sites reflected mostly no power sharing. Additionally, our findings highlight three key factors that facilitate the implementation of SDM: inclusion of veteran in treatment teams, patient education and respect for patient autonomy. Conclusions: Implementation of SDM appears feasible in acute inpatient mental health units. Although most participants were well informed about SDM, that knowledge did not always translate into practice, which supports the need for ongoing implementation support for SDM. Additional contextual factors underscore the value of patients' self-determination as a guiding principle for SDM, highlighting the role of a supporting, empowering and autonomy-generating environment.
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    Inpatient Mental Healthcare before and during the COVID-19 Pandemic
    (MDPI, 2021-11) McGuire, Alan B.; Flanagan, Mindy E.; Kukla, Marina; Rollins, Angela L.; Myers, Laura J.; Bass, Emily; Garabrant, Jennifer M.; Salyers, Michelle P.; Psychology, School of Science
    Prior studies have demonstrated disruption to outpatient mental health services after the onset of the COVID-19 pandemic. Inpatient mental health services have received less attention. The current study utilized an existing cohort of 33 Veterans Health Affairs (VHA) acute inpatient mental health units to examine disruptions to inpatient services. It further explored the association between patient demographic, clinical, and services variables on relapse rates. Inpatient admissions and therapeutic services (group and individual therapy and peer support) were lower amongst the COVID-19 sample than prior to the onset of COVID-19 while lengths of stay were longer. Relapse rates did not differ between cohorts. Patients with prior emergent services use as well as substance abuse or personality disorder diagnoses were at higher risk for relapse. Receiving group therapy while admitted was associated with lower risk of relapse. Inpatient mental health services saw substantial disruptions across the cohort. Inpatient mental health services, including group therapy, may be an important tool to prevent subsequent relapse.
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    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.
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    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.
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    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.