Effects of socialization in the household on youth susceptibility to smoking: a secondary analysis of the 2004/05 Canadian Youth Smoking Survey
Bibliographic record
Abstract
OBJECTIVE: To determine associations between younger youths' susceptibility to smoking and four household variables related to tobacco socialization: parental and sibling smoking, restrictions on smoking in the home and exposure to smoking in vehicles. METHODS: A secondary analysis of the 2004/05 Canadian Youth Smoking Survey used logistic regression to investigate the relationships between youth susceptibility to smoking, gender, and four household variables related to tobacco socialization. Susceptibility to smoking was operationalized by three levels of smoking experience and intention: non-susceptible non-smoker, susceptible non-smoker and experimenter/smoker. The national survey included 29 243 grade 5 to 9 students from randomly sampled public and private schools in ten provinces. RESULTS: For non-smokers, the odds of being susceptible to smoking increased with having a sibling who smokes, a lack of a total household smoking ban and riding in a vehicle with a smoker in the previous week, when adjusting for all other variables in the model. These variables also increased the odds of being an experimenter/smoker versus a susceptible non-smoker. Parent smoking status was not significant in these models. CONCLUSION: Denormalization messages, through enforced home and vehicle smoking bans, appear to support youth in maintaining a resolve to not smoke, regardless of parental smoking status.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".