The Prevalence of Sexual Assault Among Higher Education Students: A Systematic Review With Meta-Analyses
Bibliographic record
Abstract
Sexual assault among higher education students has detrimental impacts on the health and educational outcomes of survivors. This systematic review aims to describe and synthesize the available quantitative evidence on sexual assault prevalence among this population. We searched Medline, EMBASE, Global Health, PsycINFO, Web of Science, ERIC, and CINAHL for studies published in English, French, Italian, and Spanish from database inception to August 2020 (updated May 2022). We screened studies using prespecified inclusion criteria for the population and context (registered higher education students), condition (self-reported sexual assault), and study design (quantitative survey). The Joanna Briggs Institute Critical Appraisal Checklist was used to assess study quality. Prevalence estimates disaggregated by type of sexual assault, gender identity, and world region were meta-analyzed using a random-effects model and reported following PRISMA guidance. We identified 131 articles, from 21 different countries. The meta-analyzed prevalence of sexual assault was 17.5% for women, 7.8% for men, and 18.1% for transgender and gender diverse people. Four types of sexual assault were identified: rape, attempted rape, forced sexual touching, and coercive sex. Forced sexual touching was the most common act experienced. The African Region had the highest prevalence estimates for women's sexual assault, and the Western Pacific region had the highest prevalence estimates for men's sexual assault. Higher education institutions, especially those outside of the United States, should commit to the implementation of surveys to monitor sexual assault prevalence and dedicate increased resources to supporting student survivors of sexual assault.
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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 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".