Being Drunk to Have Fun or to Forget Problems?
Bibliographic record
Abstract
Prevention programs in adolescence are particularly effective if they target homogeneous risk groups of adolescents who share a combination of particular needs and problems. The present work aims to identify and classify risky single-occasion drinking (RSOD) adolescents according to their motivation to engage in drinking. An easy-to-use coding procedure was developed. It was validated by means of cluster analyses and structural equation modeling based on two randomly selected subsamples of a nationally representative sample of 2,449 12- to 18-year-old RSOD students in Switzerland. Results revealed that the coding procedure classified RSOD adolescents as either enhancement drinkers or coping drinkers. The high concordance (Sample A: κ = .88, Sample B: κ = .90) with the results of the cluster analyses demonstrated the convergent validity of the coding classification. The fact that enhancement drinkers in both subsamples were found to go out more frequently in the evenings and to have more satisfactory social relationships, as well as a higher proportion of drinking peers and a lower likelihood to drink at home than coping drinkers demonstrates the concurrent validity of the classification. To conclude, the coding procedure appears to be a valid, reliable, and easy-to-use tool that can help better adapt prevention activities to adolescent risky drinking motives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".