Characteristics of Physically Active Smokers and Implications for Harm Reduction
Bibliographic record
Abstract
OBJECTIVES: We sought to establish the prevalence of physical activity among smokers, whether or not physically active smokers were more likely to attempt cessation, and who these physically active smokers were. METHODS: We used logistic regression to contrast physically active and inactive smokers in a secondary data analysis of the Canadian Community Health Survey Cycle 1.1. RESULTS: Physically active smokers represented almost one quarter of the smoking population. Compared with physically inactive smokers, physically active smokers were more likely to have attempted cessation in the past year. Physically active smokers were more likely to be young, single, and men compared with their inactive counterparts. Income had no influence in distinguishing physically active and inactive smokers. CONCLUSIONS: Skepticism persists regarding the practicality and potential risks of promoting physical activity as a harm-reduction strategy for tobacco use. We found that a modest proportion of the daily smoking population was physically active and that engagement in this behavior was related to greater cessation attempts. Interventions could be developed that target smokers who are likely to adopt physical activity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".