Questioning the Social Norms Approach for Alcohol Reduction in First-Year Undergraduate Students–A Canadian Perspective
Bibliographic record
Abstract
The social norms approach to changing excessive drinking behaviour is predicated upon findings that overestimations of peer drinking predict one’s own drinking behaviour. Prior studies have yet to examine whether such social norms effects pertain equally to both genders. First-year students from a Canadian university (N = 1,155; 696 males, 459 females) were assessed for the relationship between misperceived drinking norms and hazardous drinking using the Alcohol Use Disorder Identification Test-Consumption scale (AUDIT-C). A significant positive relationship between the overestimated drinking frequency norm and hazardous drinking was determined for female students, where the odds of hazardous drinking increased by 1.92 (95% CI: 1.32–2.79) when the norm of other female students was overestimated. A non-significant association was found for male students, where the odds of hazardous drinking were unrelated to overestimation of the drinking norm of other male students. The null association for male students highlights a potential problem when using social norms interventions for alcohol reduction for males in the university context. Implications of these results for the utilization of the social norms approach to alcohol reduction are discussed.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".