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A multilevel analysis of change in alcohol consumption in Québec, 1993–98

2003· article· en· W2021836379 on OpenAlexaffabout
Andrée Demers, Sylvia Kairouz

Bibliographic record

VenueAddiction · 2003
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversité de MontréalCentre for Addiction and Mental HealthUniversité du Québec à Montréal
Fundersnot available
KeywordsDemographyPublic healthMultilevel modelVigilance (psychology)Poison controlInjury preventionAnalysis of varianceAlcohol consumptionPsychologySuicide preventionEnvironmental healthMedicineGerontologyAlcoholStatisticsMathematics

Abstract

fetched live from OpenAlex

AIMS: Changes in drinking are subgroup-specific and vary by dimensions of drinking pattern. This study investigates changes in drinking patterns between 1993 and 1998 within various subgroups of Québec society. DESIGN: A multilevel design was used in which respondents (level 1) were nested within demographic groups (level 2). PARTICIPANTS: Based on the 1993 and 1998 Québec Health and Social Surveys, 40 598 respondents (level 1) were nested within 220 groups (level 2) based on age, SES status, gender and the year of survey. MEASUREMENTS: The effect of group characteristics were assessed for various dimensions of drinking pattern. FINDINGS: A significant part of the variance in drinking pattern is attributed to group membership (ranging from 8.6% to 18.3%). Except for the frequency of heavy drinking episodes, the data revealed a significant increase between 1993 and 1998 on all drinking pattern dimensions, which was more marked for higher SES groups for current drinking status. However, the frequency of heavy drinking decreased among the lower SES groups and remained stable among the higher SES groups. CONCLUSIONS: These results call for an increase in public health vigilance in monitoring trends in drinking patterns and their potential acute and chronic consequences.

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)0.000
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 score0.094
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.092
GPT teacher head0.333
Teacher spread0.242 · 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

Citations5
Published2003
Admission routes2
Has abstractyes

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