Examining the Prevalence and Effects of Gender-based Violence in Academic Settings: A Systematic Review and Meta-analyses
Bibliographic record
Abstract
Gender-based violence (GBV) in the academic job sector is a critical issue that intersects with broader systemic and structural inequities, but research is limited. To study the prevalence, effects, and prevention measures of interpersonal GBV within the academic job sector, a meta-analysis and systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis protocol. Rigorous searches were conducted across the databases PubMed, OVID, Scopus, Web of Science, and CINAHL, using specific keywords related to GBV, workplace, and virtual work environments, identifying papers published between January 2013 and February 2023. Studies were evaluated based on the Population, Intervention, Comparison, Outcomes framework. Data from papers were extracted and grouped by reported instances, and prevalence data for interpersonal GBV were reported in university settings, including in-person, hybrid, and virtual environments, and among men, women, and those who identify as 2SLGBTQ+. A random effects meta-analysis of proportions was conducted to evaluate the reported point prevalence rates of interpersonal GBV in academia between 2012 and 2015. Subgroup analyses were performed for university staff only, females only, and males only. Out of the 1,290 records, 16 studies met the inclusion criteria. The types of violence identified include sexual harassment, workplace bullying and online harassment, which affects career advancement, and employee well-being. The meta-analyses, conducted with a 95% confidence interval [CI], identified that 51.4% (95% CI [39.9%, 63.0%]) of university staff members experience GBV, with females, 59.3% [38.1%, 80.5%], experiencing greater rates than males, 44% [28.1%, 44.1%]. The findings underscore the need for institutional interventions to address interpersonal GBV in academic workplaces.
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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 | 0.028 | 0.074 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.051 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".