Testing a Model of How a Sexual Assault Resistance Education Program for Women Reduces Sexual Assaults
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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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".