Informing population-specific smoking policy development for college campuses: An observational study
Bibliographic record
Abstract
INTRODUCTION: In Canada, young adults have the highest smoking rates among all other population groups and specifically college students are at a higher risk. To implement effective policies that can prevent smoking and increase cessation, a population-specific approach is recommended. METHODS: Smoking and non-smoking young adults enrolled in a college program were recruited. Participants who did not smoke were asked to complete questionnaires about their demographics, college experience and the college environment. Additionally, they completed The Perceived Stress Scale and The Center for Epidemiologic Studies - Depression Scale. Students who were current smokers completed the same questionnaires with the addition of one questionnaire about their smoking behaviors. Percentages, means and standard deviations were used to describe the variables of interest and a chi-squared analysis was performed, when possible, to test the difference in response frequency between smoking and non-smoking participants. RESULTS: Differences were observed between smoking (n=65) and non-smoking students (n=214). Specifically, smokers were more likely to have a family member that smoked and to participate in binge drinking. Both groups indicated that they are unaware of campus smoking regulations; however smokers were more opposed to implementing smoke-free policies. CONCLUSIONS: College students are unaware of campus smoking regulations. The descriptive information and differences observed between smoking and non-smoking students in this study should be taken into consideration when developing future smoking regulations/policies on college campuses.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".