Sex, Gender, and Alcohol Use: Implications for Women and Low-Risk Drinking Guidelines
Bibliographic record
Abstract
Alcohol use is coming under increasing scrutiny with respect to its health impacts on the body. In this vein, several high-income countries have issued low-risk drinking guidelines in the past decade, aiming to educate the public on safer levels of alcohol use. Research on the sex-specific health effects of alcohol has indicated higher damage with lower amounts of alcohol for females as well as overall sex differences in the pharmacokinetics of alcohol in male and female bodies. Research on gender-related factors, while culturally dependent, indicates increased susceptibility to sexual assault and intimate partner violence as well as more negative gender norms and stereotypes about alcohol use for women. Sex- and gender-specific guidelines have been issued in some countries, suggesting lower amounts of alcohol consumption for women than men; however, in other countries, sex- and gender-blind advice has been issued. This article reports on a synthesis of the evidence on both sex- and gender-related factors affecting safer levels of drinking alcohol with an emphasis on women's use. We conclude that supporting and expanding the development of sex- and gender-specific low-risk drinking guidelines offers more nuanced and educative information to clinicians and consumers and will particularly benefit women and girls.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".