Effects of a Tailored Social Marketing Campaign Targeting Smoking Policy Compliance on Smoking-Related Behaviour on Campus
Bibliographic record
Abstract
Introduction. Smoking represents a significant risk to Canadians. Young people in Canada have historically had the highest smoking prevalence of any other age group. Implementing smoking policies can be an effective strategy for post-secondary campuses to interrupt smoking trajectories and reduce the risk of campus citizens being exposed to second- hand smoke, however compliance can be a barrier to achieving these outcomes. This study examined the effects of a social marketing campaign on policy-non-compliance on a post- secondary campus in Ontario, Canada. Methods. The 3-week campaign was implemented by students and focused on policy- compliance-related objectives. Six smoking sites were observed twice a day for one week before the campaign, and one week after the campaign was completed. 4 sites were designated smoking areas, as defined by the smoking policy at the institution. 2 sites were undesignated “hot-spots” where smoking was frequently observed to occur. A butt litter audit was completed before and after the campaign to determine if butt litter decreased after the campaign. Results. At designated smoking sites, using the strict policy definition of the designated smoking sites, the proportion of observed behaviour that was non-compliant decreased in designated smoking areas (-0.079, 95% CI = 0.143, -0.0151, p < .05). Noncompliant behaviours also significantly decreased after the campaign using a more lenient measure of compliance (-0.102, 95% CI = -0.203, -0.001, p < .05). At undesignated hot spots, the average number of people using the areas to smoke decreased at both sites after the campaign. The proportion of all cigarettes which were disposed of correctly in receptacles was 75.5% before the campaign and 77.4% after the campaign. It is unclear if second-hand smoke exposure was reduced for non- smoking pedestrians despite the overall reduction in non-compliant behaviours. Conclusions. Implementing a student-led, social marketing campaign focussed on improving compliance was an effective strategy to improve compliance with smoking policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".