Bibliographic record
Abstract
Key Points • Given that alcohol is a leading preventable cause of death and social problems in Canada, it is important that primary care clinicians are empowered to provide the best advice to patients on alcohol use and health. • For long-term health, when it comes to consuming alcohol, the core message primary care clinicians should communicate to patients is “less is better.” • The health and safety risks associated with alcohol use are determined by the number of standard alcoholic drinks consumed per week and per occasion. A standard alcoholic drink contains approximately 13.5 grams of alcohol. • To foster supportive conversations or potentially screen for and treat alcohol use disorder when necessary, it is crucial for primary care clinicians to take a non-judgmental, equitable approach to advising patients about alcohol and health. • Anchor conversations about alcohol to the risk zones in Canada’s Guidance on Alcohol and Health: consuming 1–2 standard drinks per week is low risk, 3–6 standard drinks per week is moderate risk, and seven or more standard drinks per week is increasingly high risk. For drinks per occasion, more than two standard drinks increases short-term health risks. • Take into account special considerations about alcohol for young people under the legal drinking age, people who are pregnant, planning to become pregnant, or breastfeeding, as well as older adults. • Best practices for treating high-risk drinking and alcohol use disorder include prescribing anti-craving medications such as naltrexone and acamprosate, providing psychosocial counselling, and maintaining ongoing follow-up with patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.019 | 0.011 |
| Insufficient payload (model declined to judge) | 0.068 | 0.014 |
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".