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Record W1123052769 · doi:10.1080/15332640.2014.993784

Gender Differences in Alcohol Use and Risk Drinking in Ontario Ethnic Groups

2015· article· en· W1123052769 on OpenAlexaffabout
Branka Agic, Robert E. Mann, Andrew Tuck, Anca Ialomiteanu, Susan J. Bondy, Laura Simich

Bibliographic record

VenueJournal of Ethnicity in Substance Abuse · 2015
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsPublic Health OntarioUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsEthnic groupDemographyMedicineEnvironmental healthAlcohol consumptionAbstinenceAlcoholVulnerability (computing)Poison controlInjury preventionSuicide preventionPsychiatry

Abstract

fetched live from OpenAlex

This article examines prevalence and gender differences of alcohol use and risk drinking in a representative sample of Ontario adults. Data were drawn from the Centre for Addiction and Mental Health (CAMH) Monitor survey of Ontario adults aged 18 and older collected between January 2005 and December 2010. The prevalence of self-reported lifetime, current, and high-risk drinking were all higher among the Canadian and the European-origin groups compared with other ethnic groups. Within-group gender differences were evident for all ethnic groups. The narrowest gender gap was observed within the North European group and the widest in the South Asian group. The non-European ethnic groups had higher rates of abstinence and lower alcohol consumption rates; nevertheless, a considerable proportion of people from these groups may be at risk of alcohol-related harm due to risky and harmful alcohol consumption patterns. Future research should continue to investigate alcohol use in these groups and identify subgroups at risk and factors that increase or decrease their vulnerability to risky and problem drinking.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.812
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.179
GPT teacher head0.326
Teacher spread0.147 · 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 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

Citations10
Published2015
Admission routes2
Has abstractyes

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