What popular bars post on social media platforms: a case for improved alcohol advertising regulation
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to document the scope of violations of the Canadian Radio-television and Telecommunications Commission (CRTC) "Code for Broadcast Advertising of Alcoholic Beverages" (CRTC Code) by drinking venues posting alcohol-related content on social media platforms, and to assess whether CRTC Code violations by drinking venues relate to their popularity among university students and to students' drinking behaviours. METHODS: In phase 1 of the study, a probability sample of 477 students from four Canadian university responded to a questionnaire about their drinking and preferred drinking venues. In phase 2, a probability sample of 78 students assessed the compliance of drinking venues' social media posts with the 17 CRTC Code guidelines. We pooled both datasets and linked them by drinking venues. RESULTS: Popular drinking venues were overwhelming posting alcohol-related content that contravenes the CRTC Code. Adjusted effect estimates show that a decrease in the mean level of compliance with the CRTC Code was significantly associated with a 1% increase in popularity score of drinking venues (t-test, p < .001). With regard to drinking behaviours, a 1% increase in the overall mean level of compliance with the CRTC Code was associated with 0.458 fewer drinking days per week during a semester (t-test, p = .01), 0.294 fewer drinks per occasion (t-test, p = .048) and a lesser likelihood of consuming alcohol when attending a drinking venue (t-test, p = .001). CONCLUSION: The results of this study serve as a reminder to territorial and provincial regulatory agencies to review their practices to ensure that alcohol advertising guidelines are applied and enforced consistently. More importantly, these results call for the adoption of federal legislation with a public health mandate that would apply to all media, including print, television and radio, digital and social.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".