UK prevalence of university student and staff experiences of sexual violence and domestic violence and abuse: a systematic review from 2002 to 2022
Bibliographic record
Abstract
Research documenting the prevalence and impact of UK university students' experiences of gender-based violence (GBV) has significantly developed over the past decade, yet there has been no systematic synthesis of this evidence.This systematic review aimed to synthesise findings relating to the prevalence and impacts of GBV among staff and students in UK universities, with a focus on sexual violence (SV) and domestic violence and abuse (DVA).The search strategies involved a variety of approaches to identify both published and unpublished research, including systematic searches of electronic databases and direct contact with experts.A total of twenty-five studies focusing on SV and eight studies focusing on DVA were identified.Despite inconsistent research design, sampling frameworks, definitions and measures, and limited studies on staff experiences, review findings suggest that SV is a major issue for university students, impacting on well-being, personal relationships and academic performance.In contrast, few DVA studies were identified, many shared a range of methodological limitations, drawing on majority female samples and focusing mainly on perpetration.Validated measurement tools, consistency in study designs and sampling frameworks, which include minority student and staff populations, would strengthen current understandings of SV and DVA within UK universities.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".