Assessing the implementation of evidence-based alcohol policies on Atlantic Canadian post-secondary campuses: A comparative analysis
Bibliographic record
Abstract
OBJECTIVE: This study assessed the quality of campus alcohol policies against best practice to assist campus decision-makers in strengthening their campus alcohol policies and reducing student alcohol use and harm. METHODS: Drawing on empirical literature and expert opinion, we developed an evidence-based scoring rubric to assess the quality of campus alcohol policies across 10 alcohol policy domains. Campus alcohol policy data were collected from 12 Atlantic Canadian universities. All extracted data were verified by the institutions and then scored. RESULTS: On average, post-secondary institutions are implementing only a third of the evidence-based alcohol policies captured by the 10 domains assessed. The average campus policy score was 33% (range 15‒49%). Of the 10 domains examined, only enforcement achieved an average score above 50%, followed closely by leadership and surveillance at 48%. The two heaviest-weighted domains-availability and access, and advertising and sponsorship-had average scores of 27% and 24%, respectively. However, if post-secondary campuses adopted the highest scoring policies from across all 12 campuses, they could achieve a score of 74%, indicating improvement is possible. CONCLUSION: Atlantic Canadian universities are collectively achieving less than half their potential to reduce student alcohol-related harm. However, this study identifies opportunities where policies can be enhanced or modified. The fact that most policies are present at one or more campuses highlights that policy recommendations are an achievable goal for campuses. Campuses are encouraged to look to each other as models for improving their own policies.
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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 | 0.041 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".