Run to quit: The potential of run clinics to improve mental health in adult smokers
Bibliographic record
Abstract
Multiple health behaviour change interventions hold promise in enhancing health. In particular, increasing physical activity has been associated with improved mental health while the effect of smoking cessation has been estimated to be equal to or greater than effects of antidepressants on mood disorders. Run to Quit is a national initiative targeting physical inactivity and smoking using group run clinics in 21 locations across Canada. The purpose of this study is to describe the intervention and present baseline data. The intervention is multi-layered, and consists of several evidence-based approaches that target smoking cessation through group-based running, curriculum regarding effective ways to smoking, self-help materials, involvement of family or friends as quit buddies, etc. Implementation and outcomes of this program will be examined using a mixed methods approach. Participants will complete questionnaires assessing physical activity, smoking and quality of life at weeks 1, 3, and 10 in the 10-week program. An objective measure of smoking status will be taken pre- and post-program. Post-program interviews will be conducted with participants and coaches. Baseline demographic data suggests that the majority of participants (n = 161) are female (71%), Caucasian (91%), report being 'somewhat' to 'very stressed' (86%), and are regular smokers (avg. 12.58 cigarettes/day). Results examining associations between physical activity and quality of life at baseline will be presented. This research investigating the implementation of a multi-layered, group-level intervention will provide insights into the ability to change multiple health behaviours, and subsequent physical and mental health, of inactive smokers.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".