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Record W2010401008 · doi:10.15288/jsa.2002.63.600

For all these reasons, I do...drink: a multilevel analysis of contextual reasons for drinking among Canadian undergraduates.

2002· article· en· W2010401008 on OpenAlexaffabout
Sylvia Kairouz, Louis Gliksman, Andrée Demers, Edward M. Adlaf

Bibliographic record

VenueJournal of Studies on Alcohol · 2002
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsSituational ethicsPsychosocialContext (archaeology)PsychologyEnvironmental healthAlcohol consumptionHuman factors and ergonomicsSocial psychologyMultilevel modelSample (material)Poison controlMedicineAlcoholGeographyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: Student drinking is largely related to the setting of the drinking occasion as well as to individual psychosocial characteristics. This article assesses the effect of the reasons for drinking on situational alcohol use above and beyond other environmental and individual factors. METHOD: The data were drawn from the Canadian Campus Survey, a national mail survey conducted in 1998 with a sample of 8,864 students in 18 universities. Each student provided information on up to five drinking occasions, resulting in 25,347 drinking occasions among 6,598 drinkers. At the individual level, this study focused on the university life experience. At the situational level, information about alcohol intake was recorded relative to why, when, where and with whom drinking occurred and the reasons for drinking. RESULTS: Our results show that the reasons for drinking explain 8.3% of the variance in individual alcohol intake per occasion at the individual level and 8.1% at the drinking occasion level. CONCLUSIONS: Reasons for drinking and the drinking setting together influence consumption. Moreover, reasons are context specific, because students drink for different reasons in different contexts. Thus, contextual motivational models may be more effective in helping one understand the various pathways to alcohol use and misuse.

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)0.001
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 score0.128
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.133
GPT teacher head0.361
Teacher spread0.228 · 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

Citations134
Published2002
Admission routes2
Has abstractyes

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