Work-related stress risk and preventive measures of mental disorders in the medical environment: an umbrella review.
Bibliographic record
Abstract
OBJECTIVE: Work-related stress (WRS) is widespread among medical doctors. WRS not only affects the physician's mental and physical well-being, but also patient care quality and the overall efficiency of the healthcare system. The aim of this study is to conduct a systematic review of the current preventive measures against mental disorders, work-related stress, and burnout among physicians. MATERIALS AND METHODS: The presentation of this systematic review is in accordance with the PRISMA statement. The methodological quality of the selected studies was assessed with specific rating tools: INSA, Newcastle Ottawa Scale, JADAD scale, and AMSTAR. English publications only were selected. No restrictions applied for publication type. Reviewers excluded articles not concerning the following topics: WRS prevention, WRS risk factors and mental disorders among physicians. Reviewers also excluded findings of less academic significance, editorial articles, individual contributions, purely descriptive studies published in scientific conferences. RESULTS: Online search returned 4748 references on the following databases: PubMed (1638), Scopus (3108) and Cochrane Library (2). 36 studies were included in this review (thereof, 13 reviews and 23 original articles). Narrative reviews were rated on the INSA scale. The mean, median, and modal rating was 6. This indicates an intermediate-high quality of these studies. Systematic reviews were rated on the AMSTAR scale. The mean and median rating was 9, and the modal rating was 8. This indicates a high quality of these studies. The scores assigned to the original articles have a mean, median, and modal rating of 7. This also indicates an intermediate-high quality of these studies. CONCLUSIONS: Work-related stress and mental disorders seem to be widespread among medical practitioners. It is already a priority to adopt preventive measures against these phenomena. However, there is still no consensus on what the most effective measures are. Additional research is needed to formulate evidence-based recommendations.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".