Public Awareness of Low-Risk Alcohol Use Guidelines
Bibliographic record
Abstract
Objective. To evaluate the effectiveness of a population-based, public education campaign designed to increase awareness of the Canadian Low-Risk Alcohol Drinking Guidelines (LRDG). Method. A province-wide mass media campaign was introduced. To measure campaign effectiveness, we completed a cross-sectional study using pre- and postcampaign surveys. Measurements included awareness of the LRDG, specific knowledge of the LRDG, and beliefs toward drinking and behavior change. Results. Postsurvey respondents were more likely to be aware of the LRDG (19.2% vs. 25.8%). However, increased awareness was largely driven by females being significantly more aware of the guidelines after the campaign (odds ratio = 1.74; 95% confidence interval = [1.38, 2.19]). Men were not found to be more aware postcampaign. The results did not show a significant increase in specific knowledge of the LRDG or change in beliefs toward drinking and behavior change after the campaign. Independent of the survey cycle, males and those aged 19 to 25 years were less likely to be aware of the LRDG, select the correct drink limit or less, and believe that consuming alcohol in excess has short- and long-term health consequences when compared to females and those aged 56 to 70 years. Conclusions. A provincial public health education campaign was effective at increasing awareness of the LRDG, though uptake was lowest among those at highest risk for heavy drinking.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".