Multiple trajectories of alcohol use and the development of alcohol use disorder: Do Swiss men mature-out of problematic alcohol use during emerging adulthood?
Bibliographic record
Abstract
(A) OBJECTIVE: This study aimed to identify trajectories of alcohol use (AU) and their associations with the development of alcohol use disorder (AUD) among young men with different weekly drinking patterns. (B) METHOD: A longitudinal latent class analysis integrating several aspects of AU, such as drinking quantity and frequency on weekends vs workweek days, involving 4719 young Swiss men at ages 20, 21, and 25, and collected by the Cohort Study on Substance Use Risk Factors, was used to identify different AU trajectories over time. The development of AUD scores in these trajectories was investigated using generalized linear mixed models. (C) RESULTS: Six AU trajectory classes, similar to those described in the literature, were identified: 'abstainers-light drinkers', 'light workweek increasers', 'light decreasers', 'moderate weekend decreasers', 'moderate workweek increasers', and 'heavy drinkers'. Only 12% of participants were assigned to a trajectory class with decreasing AU associated with a decline in their AUD score. AUD scores increased in trajectory classes exhibiting increasing AU on workweek days, despite low and moderate general AU. Finally, more than 59% of participants were on an AU trajectory presenting no change in their mean AUD score over time. (D) CONCLUSIONS: Maturing out of problematic AU in emerging adulthood is not the norm in Switzerland, and the AUD score developed in late adolescence remains until at least emerging adulthood. AU on workweek days is a more practical marker of potentially problematic AU. This calls for timely interventions in adolescence and concerning regular drinking on workweek days in emerging adulthood.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".