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Record W4388941579 · doi:10.2196/47354

Integrated Alcohol Use and Sexual Assault Prevention Program for College Men Who Engage in Heavy Drinking: Randomized Pilot Study

2023· article· en· W4388941579 on OpenAlexvenueno aff
Lindsay M. Orchowski, Jennifer E. Merrill, Daniel W. Oesterle, Nancy P. Barnett, Brian Borsari, Caron Zlotnick, Michelle Haikalis, Alan D Bekowitz

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMotivational interviewingClinical psychologyPsychologyRandomized controlled trialIntervention (counseling)Social norms approachPoison controlSexual assaultSuicide preventionPsychological interventionMedicinePsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Sexual assault is prevalent on college campuses and most commonly is perpetrated by men. Problematically, there is a dearth of evidence-based prevention programs targeting men as perpetrators of sexual aggression. The Sexual Assault and Alcohol Feedback and Education (SAFE) program is an integrated alcohol and sexual assault prevention intervention for college men who engage in heavy drinking that aims to address sexual aggression proclivity and alcohol use outcomes by incorporating social norms theory, bystander intervention, and motivational interviewing. OBJECTIVE: This study aims to examine the initial feasibility-, acceptability-, and efficacy-related outcomes of a randomized pilot trial of an integrated alcohol and sexual assault prevention program for college men who engage in heavy drinking. METHODS: This study included 115 college men who engaged in heavy drinking, who were randomly assigned to the SAFE program or a mindfulness-based control condition (MBCC). The feasibility of implementation, adequacy of participant retention, fidelity and competency of program administration, and satisfaction and utility of the intervention were evaluated. The primary outcomes of alcohol use and sexual aggression were evaluated at 2 and 6 months after baseline. The secondary outcomes of perceived peer norms, risks for sexual aggression, and bystander intervention were also assessed. The extent to which the motivational interviewing session with personalized normative feedback facilitated changes in the proximal outcomes of drinking intentions, motivation to change, and self-efficacy was also examined. RESULTS: The study procedures resulted in high program completion and retention (>80%), high fidelity to the program manual (>80% of the content included), high competency in program administration, and high ratings of satisfaction and program utility in addressing sexual relationships and alcohol use. Both groups reported declines in the number of drinks per week and number of heavy drinking days. Compared with the MBCC participants, the SAFE participants reported higher motivation to change alcohol use after the program, as well as greater use of alcohol protective behavioral strategies at 6 months. Compared with the MBCC participants, the SAFE participants also reported lower perceived peer engagement in sexual coercion, perceived peer comfort with sexism, and peer drinking norms at 2 and 6 months. However, no group differences were observed in sexual aggression severity, rape myth acceptance, or the labeling of sexual consent. Results regarding bystander intervention intentions were mixed, with the MBCC group showing decreased intentions at 2 months and the SAFE group reporting increased intentions at both 2 and 6 months. CONCLUSIONS: The findings provide promising evidence for the feasibility, acceptability, utility, and preliminary efficacy of the SAFE program in reducing alcohol use and positively influencing perceived peer norms and intentions for bystander intervention among college men who drink. TRIAL REGISTRATION: ClinicalTrials.gov NCT05773027; https://clinicaltrials.gov/study/NCT05773027.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.230
GPT teacher head0.500
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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