Testing a Model of How a Sexual Assault Resistance Education Program for Women Reduces Sexual Assaults
Why this work is in the frame
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Bibliographic record
Abstract
The Enhanced Assess, Acknowledge, Act (EAAA) program has been shown to reduce sexual assaults experienced by university students who identify as women. Prevention researchers emphasize testing theory-based mechanisms once positive outcomes related to effectiveness are established. We assessed the process by which EAAA’s positive outcomes are achieved in a sample of 857 first year university students. EAAA’s goals are to increase risk detection in social interactions, decrease obstacles to risk detection or resistance with known men, and increase women’s use of effective self-defense. We used chained multiple mediator modeling to assess the combined effects of the primary mediators (risk detection, direct resistance, and self-defense self-efficacy) while simultaneously assessing the interrelationships among the secondary mediators (perception of personal risk, belief in the myth of female precipitation, and general rape myth acceptance). The hypothesized multiple mediation model with three primary mediators met the criterion for full mediation of the intervention effects. Together, the mediators accounted for 95% and 76% of the reductions in completed and attempted rape, respectively, demonstrating full mediation. The hypothesized secondary mediators were important in achieving improvements in personal and situational risk detection. The findings strongly support the benefit of cognitive ecological theory and the Assess, Acknowledge, Act conceptualization underlying EAAA. This evidence can be used by administrators and staff responsible for prevention policy and practice on campuses to defend the implementation of theoretically grounded, evidence-based prevention programs. Online slides for instructors who want to use this article for teaching are available on PWQ's website at http://journals.sagepub.com/doi/suppl/10.1177/0361684320962561
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it