Drinking guidelines are essential in combating alcohol‐related harm: Comments on the new Australian and Canadian guidelines
Bibliographic record
Abstract
The projects summarised by Room and Rehm and Stockwell et al. are important additions to the evidence base underpinning low-risk drinking guidelines for the general public and to thinking about how such guidelines should be developed, formulated and presented. In this commentary, I will firstly consider the use of guidelines in general terms and defend the proposition that they serve an essential purpose in the attempt to reduce alcohol-related harm. I will then discuss a more specific issue to do with the communication of advice on drinking—how many kinds of recommendation are needed? By coincidence, this special issue of the Drug and Alcohol Review will be published shortly after a consultation by the House of Commons Select Committee for Science and Technology on issues related to drinking guidelines for the UK. At the time of writing, the results of the consultation are unknown, but this commentary provides an opportunity to discuss the relevance of the new Australian and Canadian guidelines to the UK situation.
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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 | 0.029 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.069 | 0.066 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".