MétaCan
Menu
Back to cohort
Record W1982864757 · doi:10.1001/jama.2014.2138

Web-Based Alcohol Screening and Brief Intervention for University Students

2014· article· en· W1982864757 on OpenAlexaff
Kypros Kypri, Tina Vater, Steven J. Bowe, John B. Saunders, John Cunningham, Nicholas J. Horton, Jim McCambridge

Bibliographic record

VenueJAMA · 2014
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsCentre for Addiction and Mental Health
FundersMedical Research CouncilHealth Promotion AgencyNew Zealand GovernmentWellcome Trust
KeywordsMedicineAlcohol Use Disorders Identification TestRandomized controlled trialBinge drinkingBrief interventionAlcohol use disorderIntervention (counseling)Alcohol dependenceTest (biology)Family medicinePoison controlAlcoholPsychiatrySuicide preventionInjury preventionEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

IMPORTANCE: Unhealthy alcohol use is a leading contributor to the global burden of disease, particularly among young people. Systematic reviews suggest efficacy of web-based alcohol screening and brief intervention and call for effectiveness trials in settings where it could be sustainably delivered. OBJECTIVE: To evaluate a national web-based alcohol screening and brief intervention program. DESIGN, SETTING, AND PARTICIPANTS: A multisite, double-blind, parallel-group, individually randomized trial was conducted at 7 New Zealand universities. In April and May of 2010, invitations containing hyperlinks to the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) screening test were e-mailed to 14,991 students aged 17 to 24 years. INTERVENTIONS: Participants who screened positive (AUDIT-C score ≥4) were randomized to undergo screening alone or to 10 minutes of assessment and feedback (including comparisons with medical guidelines and peer norms) on alcohol expenditure, peak blood alcohol concentration, alcohol dependence, and access to help and information. MAIN OUTCOMES AND MEASURES: A fully automated 5-month follow-up assessment was conducted that measured 6 primary outcomes: consumption per typical occasion, drinking frequency, volume of alcohol consumed, an academic problems score, and whether participants exceeded medical guidelines for acute harm (binge drinking) and chronic harm (heavy drinking). A Bonferroni-corrected significance threshold of .0083 was used to account for the 6 comparisons and a sensitivity analysis was used to assess possible attrition bias. RESULTS: Of 5135 students screened, 3422 scored 4 or greater and were randomized, and 83% were followed up. There was a significant effect on 1 of the 6 prespecified outcomes. Relative to control participants, those who received intervention consumed less alcohol per typical drinking occasion (median 4 drinks [interquartile range {IQR}, 2-8] vs 5 drinks [IQR 2-8]; rate ratio [RR], 0.93 [99.17% CI, 0.86-1.00]; P = .005) but not less often (RR, 0.95 [99.17% CI, 0.88-1.03]; P = .08) or less overall (RR, 0.95 [99.17% CI, 0.81-1.10]; P = .33). Academic problem scores were not lower (RR, 0.91 [99.17% CI, 0.76-1.08]; P = .14) and effects on the risks of binge drinking (odds ratio [OR], 0.84 [99.17% CI, 0.67-1.05]; P = .04) and heavy drinking (OR, 0.77 [99.17% CI, 0.56-1.05]; P = .03) were not significantly significant. In a sensitivity analysis accounting for attrition, the effect on alcohol per typical drinking occasion was no longer statistically significant. CONCLUSIONS AND RELEVANCE: A national web-based alcohol screening and brief intervention program produced no significant reductions in the frequency or overall volume of drinking or academic problems. There remains a possibility of a small reduction in the amount of alcohol consumed per typical drinking occasion. TRIAL REGISTRATION: anzctr.org.au Identifier: ACTRN12610000279022.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Randomized triallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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 score0.082
Threshold uncertainty score0.198

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 head0.027
GPT teacher head0.299
Teacher spread0.272 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designRandomized trial · Other design
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

Citations121
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJAMASame topicSubstance Abuse Treatment and OutcomesFrench-language works237,207