Discrete time measures versus trajectories of drinking frequency across adolescence as predictors of binge drinking in young adulthood: a longitudinal investigation
Bibliographic record
Abstract
OBJECTIVES: We compared discrete time measures with trajectories of adolescent drinking frequency as predictors of sustained binge drinking in young adulthood. DESIGN: Prospective longitudinal study. SETTING: 10 high schools in Montréal, Canada. PARTICIPANTS: 1293 high-school students followed from mean (SD) age 12 (0.6) to 24 (0.7) years. PRIMARY OUTCOME MEASURES: Patterns of drinking frequency (self-reports every 3 months from ages 12 to 17) identified using group-based trajectory modelling. Sustained binge drinking was defined as binging monthly or more often at both ages 20 and 24. ANALYSES: Using logistic regression, sustained binge drinking was regressed on trajectory group membership and on four discrete time measures (frequency of drinking at age 12; frequency of drinking at age 17; age at drinking onset; age at onset of drinking monthly or more often). RESULTS: We identified seven drinking trajectories: late triers (15.2%), decreasers (9.5%), late escalators (10.4%), early slow escalators (16.5%), steady drinkers (14.4%), early rapid escalators (15.8%) and early frequent drinkers (18.2%). Sustained binge drinking was reported by 260 of 787 participants (33.0%) with complete data at both ages 20 and 24. Decreasers did not differ from late triers; all other patterns were associated with higher odds of sustained binge drinking (adjusted ORs: AORs=1.4-17.0). All discrete time measures were associated with sustained binge drinking, notably frequency at age 12 (a bit to try and drinking monthly: (AORs=2.6 (1.7; 3.9) and 2.8 (1.3; 6.1), respectively), age of drinking onset <13 years (AOR=7.6 (3.0; 24.1)), and any age of onset of drinking monthly or more often (AORs=5.1-8.2). CONCLUSION: Youth at risk of sustained binge drinking as young adults can be identified with indicators of early drinking as early as 7th grade (aged 12-13 years). Identification of easy-to-obtain indicators can facilitate screening and intervention efforts.
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".