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Drinking patterns, drinking contexts and alcohol‐related aggression among late adolescent and young adult drinkers

2005· article· en· W2141576953 on OpenAlexafffund
Samantha Wells, Kathryn Graham, Mark Speechley, John J. Koval

Bibliographic record

VenueAddiction · 2005
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsCentre for Addiction and Mental HealthWestern University
FundersCanadian Institutes of Health Research
KeywordsAggressionInjury preventionPoison controlContext (archaeology)Human factors and ergonomicsSuicide preventionPsychologyOccupational safety and healthEnvironmental healthDemographyPopulationMedicinePsychiatryGeography

Abstract

fetched live from OpenAlex

AIMS: The main objectives of this study were to determine: (1) the relative roles of heavy episodic drinking (HED), drinking frequency and drinking volume in explaining alcohol-related aggression and (2) whether drinking context variables (i.e. usual drinking locations, typical drinking companions and extent of peer drinking) confound or modify the relationship between HED and alcohol-related aggression or whether they predict alcohol-related aggression independently. DESIGN: A secondary analysis of the US National Longitudinal Survey of Youth was conducted. Alcohol-related aggression (denoted fights after drinking) was measured based on self-reports of arguments or fights that occurred during or after drinking in the previous 12 months. PARTICIPANTS: A composite sample of drinkers, ages 17-21, from the 1994, 1996 and 1998 Young Adult surveys (n = 738) was used. FINDINGS: Frequency of drinking and drinking volume largely confounded the association between HED and fights after drinking. Usually drinking in public locations away from home versus private locations was found to be significantly associated with a greater likelihood of fights after drinking among females. Among males, usual drinking location modified the relationship between drinking frequency and alcohol-related aggression, with the greatest risk of aggression for males who drank frequently and usually drank in public locations away from home. CONCLUSIONS: Programs designed to reduce drinking frequency in this population and to increase the safety of drinking locations in public places away from home may prove to be beneficial in reducing alcohol-related aggression.

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 score0.121
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.249
Teacher spread0.239 · 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

Citations132
Published2005
Admission routes2
Has abstractyes

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