Web‐based alcohol intervention for <scp>M</scp>āori university students: double‐blind, multi‐site randomized controlled trial
Bibliographic record
Abstract
AIMS: Like many indigenous peoples, New Zealand Māori bear a heavy burden of alcohol-related harm relative to their non-indigenous compatriots, and disparities are greatest among young adults. We tested the effectiveness of web-based alcohol screening and brief intervention (e-SBI) for reducing hazardous drinking among Māori university students. DESIGN: Parallel, double-blind, multi-site, randomized controlled trial. SETTING: Seven of New Zealand's eight universities. PARTICIPANTS: In April 2010, we sent e-mail invitations to all 6697 17-24-year-old Māori students to complete a brief web questionnaire including the Alcohol Use Disorders Identification Test (AUDIT)-C, a screening tool for hazardous and harmful drinking. Those screening positive were computer randomized to: <10 minutes of web-based alcohol assessment and personalized feedback (intervention) or screening alone (control). MEASUREMENTS: We conducted a fully automated 5-month follow-up assessment with observers and participants blinded to study hypotheses, design and intervention delivery. Pre-determined primary outcomes were: (i) frequency of drinking, (ii) amount consumed per typical drinking occasion, (iii) overall volume of alcohol consumed and (iv) academic problems. FINDINGS: Of the participants, 1789 were hazardous or harmful drinkers (AUDIT-C ≥ 4) and were randomized: 850 to control, 939 to intervention. Follow-up assessments were completed by 682 controls (80%) and 733 intervention group members (78%). Relative to controls, participants receiving intervention drank less often [RR = 0.89; 95% confidence interval (CI): 0.82-0.97], less per drinking occasion (RR = 0.92; 95% CI: 0.84-1.00), less overall (RR = 0.78; 95% CI: 0.69-0.89) and had fewer academic problems (RR = 0.81; 95% CI: 0.69-0.95). CONCLUSIONS: Web-based screening and brief intervention reduced hazardous and harmful drinking among non-help-seeking Māori students in a large-scale pragmatic trial. The study has wider implications for behavioural intervention in the important but neglected area of indigenous health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".