Bibliographic record
Abstract
INTRODUCTION: It is estimated that tobacco use kills more than 5 million people annually; it is the leading cause of preventable deaths. Recent public health interventions have likely contributed to a steady decline in rates of smoking over the past decade. Nevertheless, innovative and cost-effective approaches to smoking cessation remain a public health priority. The purpose of this study was to profile physically active smokers. METHOD: Data from the Canadian Community Health Survey 2007-2008-Ontario Sharing File were used. Responses from 41,800 persons aged 12 years and older were assessed to compare (a) the sociodemographic characteristics of physically active smokers to physically active nonsmokers in Ontario and (b) the types of leisure-time physical activities that are more commonly practiced among active Ontario smokers to active nonsmokers. RESULTS: Pearson χ(2) and independent samples t tests revealed that active smokers were more likely to be male, younger, single, and less educated and to have lower income than active nonsmokers. Active smokers were also more likely to report inexpensive, low-intensity, and solitary leisure-time physical activities. CONCLUSION: Our findings have important implications for physical activity promotion among smokers. Physical activity interventions for smokers need to be tailored differently than for nonsmokers.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".