O-390 GLOBAL SCENARIO OF GENDER-BASED VIOLENCE IN VARIOUS WORKPLACE SETTINGS: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract Background Gender-based violence (GBV) disproportionately affects young women and LGBTQ+ individuals, and therefore, a systematic review and meta-analysis was carried out to i) qualitatively identify the prevalence and experience of GBV in virtual work environments and ii) evaluate the current workplace policies, interventions or tools to protect against GBV in the workplace.“ Methods A comprehensive search was undertaken following the PRISMA guidelines after registration with PROSPERO (CRD42023399684). PubMed, OVID, Scopus, Web of Science and CINAHL databases were searched using the keywords “Gender,” “GBV,” “workplace,” and “telework.” Results Of the total 1290 studies identified after removing duplicates, 60 were included. Studies included from the global South and global North cover in-person and online/ hybrid work settings. Most of the studies were cross-sectional (42), followed by mixed-methods (10), qualitative (5) and cohort (3). The study included articles on workers in healthcare (33, 55%), academia (16, 27%), skilled trades (7, 11%), and industry (4, 7%). Discussion Bullying, undermining, and harassment (BUH) are most likely experienced by women, minority groups, and homosexuals. Workplace bullying, sexual and physical harassment, gender-based harassment, physical violence, sexual and gender discrimination, and microaggression are some forms of GBV prevalent in healthcare, academia, industry and skilled trades. Cyberbullying resulted in perceived stress and job dissatisfaction among women employees. Conclusion There must be transparency about how the institution(s) handle reports of GBV and clarity regarding the mechanisms for supporting survivors and holding perpetrators accountable. Organizations must create cyberbullying policies, standards, and processes to guarantee that the complaints are handled in a fair, confidential, and transparent manner.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.034 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".