Effectiveness of wellness program interventions to improve physician wellness: a systematic review
Bibliographic record
Abstract
BACKGROUND: Physician wellness programs are being implemented to offset rises in physician burnout. Insight into the effectiveness of these programs and to whom they are being offered, remains unclear. OBJECTIVES: To identify and characterize wellness program interventions to improve physician wellness. METHODS: A PRISMA-P 2020-compliant systematic review as conducted, searching PubMed, Scopus, and Medline from May 2006 to July 2024. Search terms included Medical Subject Headings terms and keywords related to physicians and wellness program interventions. Peer reviewed published studies that qualitatively and/or quantitively measured outcomes of wellness interventions for practicing physicians were included. RESULTS: Thirty-six studies involving 6,708 total participants were included. Interventions were heterogenous and included group therapy, stress reduction strategies, time off/workload reductions, education, and peer support. The efficacy of interventions varied, with sixteen studies (44.4%) demonstrating some measurable degree of effectiveness, with statistically significant changes (p < 0.05) post-intervention. Few studies reported improvements by physician sex, age groups, or comparisons across specialities. CONCLUSION: Studies examining physician wellness program interventions are highly heterogenous in terms of intervention, study design and methods of outcome assessment, limiting definitive conclusions about their general effectiveness. TRIAL REGISTRATION: The review protocol has been registered on Open Science Framework ( https://doi.org/10.17605/OSF.IO/8SDM9 ).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".