Rethink the Drink: Decreasing alcohol consumption through education and informed decision making
Bibliographic record
Abstract
Excessive alcohol consumption is an ongoing societal and public health concern in Canada and globally. It is apparent that there is a lack of knowledge regarding the detrimental health consequences, and the social and financial impacts alcohol misuse can cause or contribute to. Furthermore, the COVID-19 pandemic resulted in increasing alcohol sales and consumption, proving it to be the optimal time for the Canadian Government to act and advocate for the health of Canadians. Throughout our history, there have been many notable attempts to reduce alcohol consumption from methods such as temperance movements to prohibition, to educational campaigns and organizations dedicated to alcohol awareness, taxation, and purchasing age requirements. Certain efforts to decrease alcohol consumption have fallen short and some have shown promise, but it is clear that Canada still has the potential to implement effective policy change to mitigate the associated risks of alcohol misuse, and provide Canadians with the opportunity to make informed health decisions. Through research conducted for this report, one of the most promising and currently underutilized policy options to address this issue is altering labels on consumable alcohol to include health information. Health information can be presented and conveyed through labelling in various ways such as health warning statements, pictograms/images, nutrition facts, or general drinking guidelines. Amending the labelling requirements for alcohol would be a valuable addition to the existing policies in Canada, and would likely decrease alcohol consumption.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.042 | 0.010 |
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".