Adapting effective sexual assault prevention for online delivery
Bibliographic record
Abstract
Adapting effective sexual assault prevention for online delivery Can an in-person intervention that decreases young women’s risk of sexual assault maintain its effectiveness when adapted for online facilitation? Our recent research set out to answer this question. Many people have searched for ways to prevent sexual violence against women and girls, but few strategies have been found to be effective. (1) Changing societal acceptance of gender-based violence takes time, and attitude change alone does not lead to decreases in rates of violence. Efforts to prevent sexual violence perpetration have had limited results, (2) though this work continues. Dr Charlene Senn and her team had a scientific breakthrough with CIHR funding, showing that empowering young women through resistance education can decrease their risk of sexual assault and intimate partner violence (IPV) by 50%. (3,4,5) The intervention was the Enhanced Assess, Acknowledge, Act (EAAA; also known as Flip the Script with EAAA®), a 12-hour program delivered in small groups by two expert near-peers on university campuses. Implementation is resource- intensive for universities (e.g., training and staffing costs) even though the program itself is available at cost (SARE Centre). It is the only intervention that has demonstrated large, long- lasting reductions in sexual and IPV victimization. It has been used on campuses in five countries; however, the reach is still limited.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".