Changing the Smoking Trajectory: Evaluating the Impact of School-Based Tobacco Interventions on Changes to Susceptibility to Future Smoking
Bibliographic record
Abstract
School-based programs and policies can reduce student smoking rates. However, their impact on never-smoking students has not been investigated despite the clear transition between non-susceptible, susceptible, and ever tried smoking statuses. The objective of this paper was to examine the longitudinal student-level impact of six changes in school-based tobacco control programs and policies on student transitions in susceptibility to smoking over one year. Two multinomial logistic regression models identified the relative risk of a change in self-reported susceptibility to smoking or in trying a cigarette among never-smoking students in each of the six intervention schools compared to the relative risk among never-smoking students in control schools. Model 1 identified the relative risk of a change in smoking susceptibility status among baseline non-susceptible never smoking students, while Model 2 identified the relative risk of a change in smoking susceptibility status among baseline susceptible never smoking students. Students at some intervention schools were at increased risk of becoming susceptible to or trying a cigarette at one year follow-up. Intervention studies should examine changes to susceptibility to future smoking when evaluating impact to ensure that school-based tobacco control programs and policies do not negatively change the risk status of never-smoking students.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".