Predictors and Consequences of Veterans Affairs Mental Health Provider Burnout: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: In the Veterans Health Administration (VHA), mental health providers (MHPs) report the second highest level of burnout after primary care physicians. Burnout is defined as increased emotional exhaustion and depersonalization and decreased sense of personal accomplishment at work. OBJECTIVE: This study aims to characterize variation in MHP burnout by VHA facility over time, identifying workplace characteristics and practices of high-performing facilities. METHODS: Using both qualitative and quantitative methods, we will evaluate factors that influence MHP burnout and their effects on patient outcomes. We will compile annual survey data on workplace conditions and annual staffing as well as productivity data to assess same and subsequent year provider and patient outcomes reflecting provider and patient experiences. We will conduct interviews with mental health leadership at the facility level and with frontline MHPs sampled based on our quantitative findings. We will present our findings to an expert panel of operational partners, Veterans Affairs clinicians, administrators, policy leaders, and experts in burnout. We will reengage with facilities that participated in the earlier qualitative interviews and will hold focus groups that share results based on our quantitative and qualitative work combined with input from our expert panel. We will broadly disseminate these findings to support the development of actionable policies and approaches to addressing MHP burnout. RESULTS: This study will assist in developing and testing interventions to improve MHP burnout and employee engagement. Our work will contribute to improvements within VHA and will generate insights for health care delivery, informing efforts to address burnout. CONCLUSIONS: This is the first comprehensive, longitudinal, national, mixed methods study that incorporates different types of MHPs. It will engage MHP leadership and frontline providers in understanding facilitators and barriers to effectively address burnout. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/18345.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.039 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.068 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".