Changes in smoking behaviors from late childhood to adolescence: Insights from the Canadian National Longitudinal Survey of Children and Youth.
Bibliographic record
Abstract
OBJECTIVE: To examine smoking behaviors in Canadian youth from late childhood to adolescence. By following participants from as young as 10 and 11 years, the authors proposed to identify distinct developmental pathways of smoking acquisition. DESIGN: Growth mixture modeling was used to identify developmental trajectories of smoking among 10- to 17-year-old participants of the Canadian National Longitudinal Survey of Children and Youth. MAIN OUTCOME MEASURES: Developmental trajectories of trying smoking, smoking frequency, and smoking intensity. RESULTS: Five developmental trajectories related to smoking frequency were identified, of which 2 were acquisition patterns that led to daily smoking at age 16-17, and 3 were experimentation patterns that led to nonsmoking at age 16-17. The largest variability in changes in smoking behavior over time was the reported level of smoking frequency. CONCLUSION: Analysis showed that there is more than 1 way in which Canadian children and adolescents acquire smoking behaviors over time. The authors were able to differentiate patterns of experimentation from patterns of acquisition. Whereas experimentation has been generally considered as 1 of the stages in the smoking acquisition process leading to regular smoking, these results indicate that experimentation can be described as a distinct process in itself.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".