PREDICTORS OF DRINKING BEHAVIOUR AMONG ADOLESCENTS AND YOUNG ADULTS: A NEW PSYCHOSOCIAL CONTROL PERSPECTIVE
Bibliographic record
Abstract
Based on common cause conceptualisations of problem behaviour, we examined whether a revised psychosocial control theory of adolescent delinquency could explain problem drinking among a non-clinical convenience sample of adolescents and young adults. A sample of 329 Australian secondary school students (adolescent age groups 13–14 and 15–17, 50.6% female) and 334 Australian university students (age groups 18–20 and 21–24, 68.4% female) in Canberra, Australia participated in an online survey comprising self-reported problem drinking and psychosocial control measures. The revised psychosocial model explained variance in problem drinking with large effect sizes in all four age cohorts. Peer risk-taking behaviours significantly predicted problem drinking across all age cohorts, and impulsivity was more influential than sensation seeking. While the findings partially support a revised psychosocial control model, psychosocial control risk factors need to be considered along with the broader sociocultural context. This is particularly important in Australia where drinking is often considered normative within universities and the general community.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".