A gender-based review of workplace violence amongst the global health workforce—A scoping review of the literature
Bibliographic record
Abstract
Workplace violence (WPV) impacts all levels of the health workforce, including the individual provider, organization, and society. While there is a substantial body of literature on various aspects of WPV against the health workforce, gender-based WPV (GB-WPV) has received less attention. Violence in both the workplace and broader society is rooted in gendered socio-economic, cultural, and institutional factors. Developing a robust understanding of GB-WPV is crucial to explore the differing experiences, responses, and outcomes of GB-WPV with respect to gender. We conducted a scoping review and report on the prevalence and risk factors of GB-WPV in healthcare settings globally. The review followed the Preferred Reporting Items for Systematic and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). We registered the scoping review protocol on the Open Science Framework on January 14, 2022, at https://osf.io/t4pfb/. A systematic search was conducted of empirical literature in five health and social science databases. Of 13667, 226 studies were included in the analysis. Across the studies, more women than men experienced non-physical violence, including verbal abuse, sexual harassment, and bullying. Men experienced more physical violence compared to women. Younger age, less experience, shifting duties, specific clinical settings, lower professional status, organizational hierarchy, and minority status were found to be sensitive to gender, reflecting women's structural disadvantages in the workplace. Given the high prevalence and impact of GB-WPV on women, we provided recommendations to address systemic issues in clinical practice, academia, policy, and research.
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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 | 0.019 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.030 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".