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Record W2068745324 · doi:10.1177/009145091304000203

College Alcohol-Control Policies and Students' Alcohol Consumption: A Matter of Exposure?

2013· article· en· W2068745324 on OpenAlexaboutno aff
Andrée Demers, Nancy Beauregard, Louis Gliksman

Bibliographic record

VenueContemporary Drug Problems · 2013
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsResidenceEnvironmental healthAlcohol consumptionPsychologyEpidemiologyDifferential (mechanical device)Higher educationMental healthAddictionMultilevel modelGerontologyAlcoholDemographyMedicineSociologyPolitical sciencePsychiatryEngineering

Abstract

fetched live from OpenAlex

The aims of this study were twofold: a) to investigate the impact of higher education institutional alcohol-control policies on students' drinking, and b) to determine whether a differential exposure to such policies based on students' place of residence (on/off campus) was a significant source of variability in their drinking practices and patterns. The data was drawn from the 2004 Canadian Campus Survey, a large epidemiological survey examining the social determinants of addiction and mental health among full-time undergraduates enrolled in Canadian universities (N = 4,358). Multilevel analyses performed on samples stratified by place of residence evaluated differences in explanatory factors for drinking practices (probability of drinking on campus) and patterns (usual drinking quantity). Overall, alcohol-control policies distinctively contributed to explain outcomes among campus residents and off-campus residents. Results suggest that the place of residence is an important determinant modulating students' drinking outcomes and interactions with higher education institutions.

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)0.001
metaresearch head score (Gemma)0.009
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 score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.035
GPT teacher head0.286
Teacher spread0.251 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

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