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Record W4224250966 · doi:10.3390/ijerph19084523

Sex, Gender, and Alcohol Use: Implications for Women and Low-Risk Drinking Guidelines

2022· review· en· W4224250966 on OpenAlexafffund
Lorraine Greaves, Nancy Poole, Andreea C. Brabete

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2022
Typereview
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsBritish Columbia Centre of Excellence for Women's HealthUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsScrutinyInjury preventionPublic healthMedicineSuicide preventionEnvironmental healthAlcoholHuman factors and ergonomicsOccupational safety and healthPoison controlAlcohol consumptionPsychologyPolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.389
GPT teacher head0.506
Teacher spread0.117 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations55
Published2022
Admission routes2
Has abstractyes

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