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Record W3200972860 · doi:10.24095/hpcdp.41.9.04

Prevalence of alcohol use among women of reproductive age in Canada

2021· article· en· W3200972860 on OpenAlexaffvenueabout
Mélanie Varin, Elia Palladino, Kate Hill MacaEachern, Lisa Belzak, Melissa M. Baker

Bibliographic record

VenueHealth Promotion and Chronic Disease Prevention in Canada · 2021
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsCarleton UniversityPublic Health Agency of Canada
Fundersnot available
KeywordsEnvironmental healthMedicineResidenceAlcoholAlcohol consumptionCannabisPopulationDemographySubstance usePregnancyPsychiatry

Abstract

fetched live from OpenAlex

Introduction Reporting on alcohol use among women of reproductive age in Canada addresses a major gap in evidence. Methods We assessed the prevalence of weekly and heavy alcohol consumption among women aged 15 to 54 years by sociodemographic characteristics, province of residence and concurrent use of other substance(s) using data from the 2019 Canadian Community Health Survey. Results Of the target population, 30.5% reported weekly and 18.3% reported heavy alcohol consumption in the past year. Prevalence varied by sociodemographic characteristics, province and substance use. The most notable and significant differences were to do with cannabis use and smoking. Conclusion This information can guide health care providers in assessing alcohol consumption and in promoting low-risk alcohol drinking to prevent alcohol exposure during pregnancy.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.295
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations9
Published2021
Admission routes3
Has abstractyes

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