Prevention of sexual violence and domestic abuse through a university bystander intervention programme: learning from a UK feasibility study
Bibliographic record
Abstract
In response to evidence documenting the scale and impact of sexual violence (SV) and domestic abuse (DA) in universities, Universities UK (2016) recommend implementation of a UK based bystander programme, The Intervention Initiative (TII), as a key prevention strategy. However, a recent UK review (Gaffney et al, 2023) concluded that no studies have addressed implementation issues for university-based bystander programmes. Our study explored what is required for implementation of the TII in a UK university, rather than intervention effectiveness. The intervention was delivered to undergraduate students across three school cohorts: medicine, social work and sports coaching. The study draws on pre- and post-intervention surveys to explore SV and DA knowledge, attitudes, and bystander skills. Focus groups or individual interviews with students (n=11) and staff facilitators (n=10) explored experiences of implementation, delivery and participation. Students reported positive changes across several areas and some evidence of immediate impact on behaviours, suggesting potential for wider implementation across university contexts. Barriers included professionalisation of the application of the bystander intervention, resistance to an underpinning gendered evidence base and a lack of diversity and relatability in programme materials.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".