School Connectedness and Susceptibility to Smoking Among Adolescents in Canada
Bibliographic record
Abstract
INTRODUCTION: Smoking susceptibility in early adolescence is strongly predictive of subsequent smoking behavior in youth. As such, smoking susceptibility represents a key modifiable factor in reducing the onset of smoking in young people. A growing literature has documented a number of factors that influence susceptibility to smoking; however, there is limited amount of research examining associations of susceptibility to smoking and school connectedness. The current study examines whether school connectedness has an independent protective effect on smoking susceptibility among younger adolescents. METHODS: A nationally representative sample of 12,894 Canadian students in grades 6-8 (11-14 years old), surveyed as part of the 2010-2011 Youth Smoking Survey, was analyzed. Multilevel logistic regression models examined unadjusted and adjusted associations between school connectedness and smoking susceptibility. The impacts of other covariates on smoking susceptibility were also explored. RESULTS: Approximately 29% of never-smokers students in grades 6-8 in Canada were susceptible to future smoking. Logistic regression analysis, controlling for standard covariates, found that school connectedness had strong protective effects on smoking susceptibility (odds ratio [OR] 0.91, 95% CI 0.89-0.94). CONCLUSIONS: The finding that school connectedness is protective of smoking susceptibility, together with previous research, provides further evidence that improving school conditions that promote school connectedness could reduce risky behavior in adolescents. While prevention efforts should be directed at youth of all ages, particular attention must be paid to younger adolescents in the formative period of 11-14 years of age.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".