O-134 ADDRESSING HEALTHCARE WORKERS’ MENTAL HEALTH: A SYSTEMATIC REVIEW OF EVIDENCE-BASED INTERVENTIONS
Bibliographic record
Abstract
Abstract Introduction Mental health issues (e.g., distress, burnout) and mental illnesses (e.g., anxiety, depression) are associated with increased absenteeism and presenteeism (i.e., lost productivity or reduced performance), turnover, and increased rates of short-term and long-term disability in the workforce. Our objective is to provide a systematic review of intervention literature focused on the support and treatment of mental health within the healthcare workforce, as a guide to hospital administrators grappling with these problems. Methods Databases (e.g., Ovid, Medline, PsycINFO were searched using terms representing the target population (e.g., physicians, nurses, specialists), mental health outcomes (e.g., burnout, depression, anxiety), and intervention type (e.g., training program). Search was unbounded through March, 2022. Results Of 5,082 publications screened, 120 interventions were included. Randomized Control Trials were used by 54 studies (45%). Studies were conducted in 26 countries; most interventions were conducted in the US (45; 38%). Of the 77 interventions, 92(77%) reported significant effects and 47 (39%) reported measurable effect sizes. Twenty-nine interventions significantly reduced stress (24%), 19 reduced anxiety (16%), 15 reduced depression (13%), 16 reduced burnout (13%) and 17 reduced emotional exhaustion/compassion fatigue (14%). Discussion Twenty-eight of the 120 interventions produced at least one improved outcome that met the criterion for a large effect size. Most interventions focused on helping the individuals cope with or avoid mental health problems; few were focused on improving the workplace to support employee health. Conclusion Targeted, well-designed workplace mental health interventions can improve mental health outcomes among healthcare workers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".