Web-Based Alcohol and Sexual Assault Prevention Program With Tailored Content Based on Gender and Sexual Orientation: Preliminary Outcomes and Usability Study of Positive Change (+Change)
Bibliographic record
Abstract
BACKGROUND: Alcohol use and sexual assault are common on college campuses in the United States, and the rates of occurrence differ based on gender identity and sexual orientation. OBJECTIVE: We aimed to provide an assessment of the usability and preliminary outcomes of Positive Change (+Change), a program that provides integrated personalized feedback to target alcohol use, sexual assault victimization, sexual assault perpetration, and bystander intervention among cisgender heterosexual men, cisgender heterosexual women, and sexual minority men and women. METHODS: Participants included 24 undergraduate students from a large university in the Southwestern United States aged between 18 and 25 years who engaged in heavy episodic drinking in the past month. All procedures were conducted on the web, and participants completed a baseline survey, +Change, and a follow-up survey immediately after completing +Change. RESULTS: Our findings indicated that +Change was acceptable and usable among all participants, despite gender identity or sexual orientation. Furthermore, there were preliminary outcomes indicating the benefit for efficacy testing of +Change. CONCLUSIONS: Importantly, +Change is the first program to target alcohol use, sexual assault victimization, sexual assault perpetration, and bystander intervention within the same program and to provide personalized content based on gender identity and sexual orientation. TRIAL REGISTRATION: ClinicalTrials.gov NCT04089137; https://clinicaltrials.gov/ct2/show/NCT04089137.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".