SCHOOL MATTERS: DRINKING DIMENSIONS AND THEIR EFFECTS ON ALCOHOL-RELATED PROBLEMS AMONG ONTARIO SECONDARY SCHOOL STUDENTS
Bibliographic record
Abstract
AIMS: To test the hypotheses that average volume of alcohol consumption and patterns of drinking, each influence alcohol-related problems and that both act at individual and aggregate levels. METHODS: The 2003 cycle of the Ontario Student Drug Use Survey obtained self-administered questionnaires from a representative classroom-based survey of 2455 Ontario secondary school students (grades 9-12) from 74 schools, with a student completion rate of 72%. Average volume of alcohol consumption was assessed using a quantity-frequency measure. Heavy drinking occasions were operationalized by four dummy variables indicating less than monthly, monthly, weekly and daily consumption of five or more drinks per occasion, with never having a heavy drinking occasion serving as the reference group. Alcohol-related problems were measured by using seven items of the Alcohol Use Disorders Identification Test. RESULTS: As hypothesized, both the average volume of alcohol consumption and patterns of drinking influenced alcohol-related problems at the student level, independently of each other. At the school level, both determinants significantly influenced the problems, but not when simultaneously entered into the equation. CONCLUSIONS: Future prevention of alcohol-related problems in adolescents should consider both the average volume and patterns of drinking. Both prevention and research should also try to include environmental determination of alcohol-related problems.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".