Burnout Interventions for Resident Physicians: A Scoping Review of Their Content, Format, and Effectiveness
Bibliographic record
Abstract
CONTEXT.—: Physicians face a high rate of burnout, especially during the residency training period when trainees often experience a rapid increase in professional responsibilities and expectations. Effective burnout prevention programs for resident physicians are needed to address this significant issue. OBJECTIVE.—: To examine the content, format, and effectiveness of resident burnout interventions published in the last 10 years. DESIGN.—: The literature search was conducted on the MEDLINE database with the following keywords: internship, residency, health promotion, wellness, occupational stress, burnout, program evaluation, and program. Only studies published in English between 2010 and 2020 were included. Exclusion criteria were studies on interventions related to the COVID-19 pandemic, studies on duty hour restrictions, and studies without assessment of resident well-being postintervention. RESULTS.—: Thirty studies were included, with 2 randomized controlled trials, 3 case-control studies, 20 pretest and posttest studies, and 5 case reports. Of the 23 studies that used a validated well-being assessment tool, 10 reported improvements postintervention. These effective burnout interventions were longitudinal and included wellness training (7 of 10), physical activities (4 of 10), healthy dietary habits (2 of 10), social activities (1 of 10), formal mentorship programs (1 of 10), and health checkups (1 of 10). Combinations of burnout interventions, low numbers of program participants with high dropout rates, lack of a control group, and lack of standardized well-being assessment are the limitations identified. CONCLUSIONS.—: Longitudinal wellness training and other interventions appear effective in reducing resident burnout. However, the validity and generalizability of the results are limited by the study designs.
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 | 0.024 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".