Disruptiveness, peer experiences and adolescent smoking: a long‐term longitudinal approach
Bibliographic record
Abstract
AIMS: This study examined links of peer experiences (i.e. social status and affiliation with disruptive peers) throughout childhood with respect to adolescent smoking trajectories, after controlling for childhood disruptiveness. Specifically, we tested four models regarding links of peer experiences and deviant behaviours. DESIGN: Prospective community sample. PARTICIPANTS: A total of 312 children, aged 6.17 years at baseline. MEASUREMENTS: Growth parameters of own disruptive behaviour, disruptive behaviour of friends and social status measured at ages 7-12 years as predictors of smoking assessed at ages 13-15 years, while controlling for own disruptive behaviour at age 6 years. FINDINGS: We found three groups with distinct profiles of smoking. One group displayed hardly any or no smoking at all; a second group showed a trajectory of increased smoking; and a third group that showed high smoking rates initially and increased in smoking intensity over time. Results support the assumption of the selection model that the link between disruptive peers and smoking is spurious and due to shared variances with own early disruptiveness. Moreover, support was found for the popularity-socialization model supporting the assumption that age-related increases in social status are associated with smoking. CONCLUSIONS: The findings emphasize that early disruptiveness is predictive of later smoking. In addition, it was shown that smoking becomes less deviant over time, in line with group norms. Future prevention programmes should emphasize the need to change these norms.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".