Prevalence of Type II and Type III Workplace Violence against Physicians: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Workplace violence (WPV) in the health care sector remains a prominent, under-reported global occupational hazard and public health issue. OBJECTIVE: To determine the types and prevalence of WPV among doctors. METHODS: Primary papers on WPV in medicine were identified through a literature search in 4 health databases (Ovid Medline, EMBASE, PsychoINFO and CINAHL). The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for the mapping and identification of records. To assess the studies included in our review, we used the Critical Appraisal Skills Programme cohort review checklist and the Risk of Bias Assessment. RESULTS: 13 out of 2154 articles retrieved were reviewed. Factors outlining physician WPV included (1) working in remote health care areas, (2) understaffing, (3) mental/emotional stress of patients/visitors, (4) insufficient security, and (5) lacking preventative measures. The results of 6 studies were combined in a meta-analysis. The overall prevalence of WPV was 69% (95% CI 58% to 78%). CONCLUSION: The impact of WPV on health care institutions is profound and far-reaching; it is quite common among physicians. Therefore, steps must be taken to promote an organizational culture where there are measures to protect and promote the well-being of doctors.
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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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".