Determinants of stress on resident physicians: systematic review and meta-synthesis
Bibliographic record
Abstract
Stress/burnout on resident physicians is common. Various determinants can be related to resident stress. This systematic review was conducted to determine how situational, personal, or professional determinants influence resident stress. We identified an English and Indonesia articles using online database including PubMed, Wiley Online Library, Google Scholar, Garba Rujukan Digital (GARUDA), and manually searching bibliographies of the included studies from January 01, 2001 until April 30, 2021. Three main search terms included are resident physician, determinant, and stress/burnout. Study selection included was peer-reviewed literatures of observational studies that discuss about stress determinants on residents from various year of training and medical specialties. Methodological quality of studies was assessed using Newcastle-Ottawa Scale adopted for cross-sectional studies. Data extraction conducted by 3 authors. All pooled synthesis were summarized based on narrative methods. Fifty-three cross-sectional, 1 prospective, and 1 combination of cross-sectional and longitudinal studies meet our inclusion criteria (n=29.031). Fifty-one percent are male, and the average age of the participants was 29 years old. The most stress/burnout validated tool used are Maslach Burnout Inventory. The average quality of study was moderate for cross-sectional studies. The main identified determinant was situational, the second was personal, and the latter was professional. The most stressor identified was ‘excessive working time per week, includes night shift, on-call, work on day off, and rotation more than 24 hours.’ Stress/burnout on residents closely related mainly to situational, followed by personal, and less by professional determinants. There was needed for an intervention to the educational program from institution in the future for better accomplishment.
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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 | 0.033 | 0.107 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.024 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 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".