Recommendations for Tobacco Control on Post-Secondary Campuses that are Geographically Isolated
Bibliographic record
Abstract
BACKGROUND: Many Ontarians continue to report exposure to second-hand smoke in public spaces. Completely smoke-free environments are the preferred and socially responsible option for non-smoking policies; however, when considering the variety of landscapes in which post-secondary institutions are located, 'a one size fits all' smoking policy is unrealistic to implement and enforce. The purpose of the study was to: 1) gain a better sense of the prevalence of smoking and exposure to second-hand smoke in a post-secondary context that is geographically isolated; 2) assess the awareness of existing non-smoking initiatives; and 3) identify preferred approaches for tobacco control. METHODS: An online survey was distributed in 2012 to all members of the Laurentian University community. Descriptive statistics are presented, using frequency distributions, and group comparisons are reported, using Chi-Square analyses. RESULTS: A total of 1282 persons completed the survey. Nearly 80% of respondents reported that they had been exposed to second-hand smoke in the past month on campus and the majority of respondents felt that smoking should only be allowed in Designated Outdoor Smoking Areas (51.5%); including 37.3% of daily smokers and occasional smokers. CONCLUSION: Institutions with a geographically isolated campus, which limit options to divert smokers from public entrances, should consider the use of Designated Outdoor Smoking Areas. Implementation will create immediate reductions in the prevalence of smoking at building entrances and in high traffic locations and will therefore protect non-smokers from the dangers of environmental tobacco smoke.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.038 | 0.011 |
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".