Tobacco retailer density surrounding schools and youth smoking behaviour: a multi-level analysis
Bibliographic record
Abstract
BACKGROUND: Youth smoking prevention should be a public health priority. It is not only vital to prevent youth from smoking but also to prevent non-smoking youth from becoming susceptible to smoking. Past research has examined factors associated with youth's susceptibility to become a future smoker, but research has yet to examine tobacco retailer density and susceptibility to smoking among never smokers. The objectives of this study are to examine how tobacco retailer density surrounding schools and social smoking influences are associated with smoking susceptibility among youth of never smokers, and occasional and daily smoking among youth of current smokers. METHODS: Data were collected in 2005-2006 from grade 9 to 12 students attending 76 secondary schools in Ontario, Canada, as part of the SHAPES-On study. A series of multi-level logistic regression analyses were performed to understand how student- and school-level factors are associated with three smoking behaviour outcomes: smoking susceptibility among never smokers, occasional smoking, and daily smoking. RESULTS: The number of tobacco retailers surrounding a school was found to be associated with the likelihood of a never smoker being susceptible to future smoking (OR 1.03, 95CI% 1.01, 1.05). We also identified that being surrounded by smoking social influences, specifically family and close friends, can substantially increase the likelihood that never smokers are at risk for future smoking or that youth are already occasional or daily smokers. CONCLUSIONS: We identified that the number of tobacco retailers surrounding a school was associated with an increased odds of being susceptible to future smoking among male never smokers. Smoking social models surrounding youth also appears to have an important impact on their smoking behaviour regardless of their smoking status. It is important for youth smoking prevention programs to begin early, interrupt youths' susceptibility to future smoking, and focus on subgroups that are at higher risk of smoking. The government should consider the impact of tobacco retailer density on youth smoking behaviour, and be cautious when granting licenses for establishments to sell tobacco products.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".