Disentangling gender and age effects on risky single occasion drinking during adolescence
Bibliographic record
Abstract
OBJECTIVES: To determine the association between adolescent risky single occasion drinking (RSOD) and gender, age, and the relative age position of students within their class. METHOD: A cross-sectional national representative sample of 7088 from 8th to 10th graders in Switzerland (mean age 14.6; SD = 0.94) as part of the ESPAD international study was analysed using hierarchical linear modelling. RESULTS: Being male, older than the class average and a member of older classes (on average) was associated with a higher RSOD frequency. Additionally, interactions between student and class level were found. The higher the mean class age the greater the difference in RSOD between boys and girls, and the lower the impact on RSOD of the relative age position in a class. CONCLUSIONS: In early and mid-adolescence, prevention efforts should try to impede the modelling of alcohol use of older classmates, whereas in late adolescence gender-specific motives for RSOD should be taken into account.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".