SMOKING BEHAVIORS AND ATTITUDES TOWARDS THE SMOKE-FREE CAMPUS POLICY: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
We conducted this systematic review to document the published literature related to smoking behaviors and attitudes towards the smoke-free campus policy. Studies on universities that had implemented the smoke-free campus policy were included in this review. The search for published articles from January 2010 to December 2020 involved three main electronic databases: Ovid MEDLINE, ScienceDirect, and Scopus. We searched the databases with the following Boolean string: [(smoke-free OR tobacco-free) AND (campus OR university OR college) AND (knowledge* OR attitude* OR practice*)]. Seventeen studies were included in this review. The majority (n = 8) were from the United States, followed by Saudi Arabia (n = 2) and one each from Israel, Lebanon, Australia, Canada, the United Kingdom, Spain, and China. Eight studies reported a positive impact of the policy on smoking behavior (plan to quit smoking, attempt to quit smoking, reduce smoking). However, 11 studies reported that respondents were still exposed to second-hand smoke and that cigarette butts were still scattered around the university area. Nine studies reported negative attitudes towards smoking, and seven of 12 studies reported positive attitudes towards the policy. Overall, the smoke-free campus policy had mixed impacts. Nevertheless, we found that attitude towards a smoke-free campus and smoking behavior has a good impact.
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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".