Smoking zones versus smoke-free zones on Canadian post-secondary campuses: Which zone is more effective, adhered to and preferred?
Bibliographic record
Abstract
INTRODUCTION: This study aims to determine if smokers at post-secondary campuses are more likely to adhere to smoke-free zones (areas where smoking is not permitted) or smoking zones (areas where smoking is permitted) based on preference and effectiveness. METHODS: A self-reported survey was developed and administered at two postsecondary institutions; Western University (smoke-free zones) and Fanshawe College (smoking zones). Smokers were asked how often they use these zones, which zone is preferred and which zone they think is more effective. A chi-squared analysis was performed to determine if there were differences in the frequency of responses. RESULTS: A total of 239 surveys were collected, 119 from Western and 120 from Fanshawe. Of these, 87% of respondents at Fanshawe were aware of where they could smoke on campus, and 67% reported that they mostly or always used these spaces. At Western, significantly fewer respondents knew where to smoke (57%), and only 30% reported mostly or always using appropriate zones (p<0.05). More participants at Fanshawe indicated that they had been told by someone in authority where they could smoke (36%) compared to Western (19%, p<0.05). At Fanshawe, 63% of respondents stated that smoking zones mostly or always effectively indicated where it was appropriate to smoke on campus compared to only 18% at Western (p<0.05). Both groups indicated they preferred the zone they currently had. Finally, more participants from Fanshawe intend to quit smoking within 6 months (61% from Fanshawe vs 49% from Western, p<0.05). CONCLUSIONS: Smoking zones on post-secondary campuses may be more effective and adhered to by smokers than smoke-free zones.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".