Integrating Alcohol and Cannabis Risk Reduction Into Sexual Assault Resistance Programming: Findings From a Pilot of EAAA+
Bibliographic record
Abstract
OBJECTIVE: One in five college women experiences sexual assault. Feminist scholars have called for the use of programming that empowers women by increasing their ability to recognize and resist sexual assault. One such program, the Enhanced Assess, Acknowledge, Act Sexual Assault Resistance Education Program (EAAA), has demonstrated lower rates of sexual assault up to 24 months. EAAA could be further enhanced by directly targeting women's risky alcohol and cannabis use, two known risk factors for sexual assault, within an integrated framework. This study evaluated the acceptability and preliminary efficacy of an integrated version of EAAA with enhanced alcohol and new cannabis content. METHOD: = 14) ages 18-24 who reported engaging in past-month heavy alcohol use, cannabis use, and simultaneous alcohol and cannabis use participated in the adapted program. Women completed a baseline assessment, measures of acceptability at strategic points during the program, and a post-program assessment. RESULTS: Women rated the program very high in likability, quality, organization, relevance, and usefulness and were likely to recommend it to other women. Overall, acceptability and usability ratings for EAAA+ were high. Content analysis of open-ended questions indicated that some women wanted more cannabis and/or alcohol content included. CONCLUSIONS: Results indicate that the adapted content is acceptable for the target population and has promising pre-post changes on alcohol, cannabis, and sexual assault-related outcomes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".