Coping strategies to prevent or reduce stress and burnout among oncology physicians: a systematic review
Bibliographic record
Abstract
ABSTRACT The purpose of this systematic review (SR) was to identify interventions that are effective to prevent or reduce stress and burnout among oncologists. Search was conducted in eight electronic databases and grey literature databases, with no language or time restrictions. Included studies involved medical oncologists and contained interventions to prevent or deal with stress or burnout with outcomes assessment. In two selection phases process, 19 out of 3,020 studies were included. Risk of bias was low for nine studies, moderate for six studies and high for four ones. Certainty of evidence was considered low and very low for the analyzed outcomes. Interventions varied a lot and those which had a significant effect in stress and burnout reduction among oncologists were experience sharing between female doctors in virtual groups, integrative meetings outside the work environment, and team sessions supervised by counselors. Although interventions had variable effects on reducing or preventing burnout and stress, mores studies are needed due to outcomes low evidence.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".