‘When you put the Group and the Running Together. . .’: A Qualitative Examination of Participant Experiences of the Canadian Run to Quit program
Bibliographic record
Abstract
Introduction: Run to Quit is a national community-based program that combines smoking cessation support with physical activity through learn to run group-based curriculum, self-help and smoking cessation materials. The program is currently in a three-year scaling up phase. Aims: The aim of the current study is to explore participant experiences of the Run to Quit program after its first year, and identify potential areas of improvement for future iterations of the program. Methods: Participants (n = 55) were interviewed over the phone at the end of the 10-week program. Participant interviews were recorded and transcribed. A thematic analysis was conducted. Results/Findings: Participants were satisfied with the program. Strengths of the program were the group aspect, supervised participation and the running. Weaknesses were seen as the variability in walking and running abilities and inadequate engagement by the Smokers Helpline. Many people who successfully quit smoking reported using additional quit aids. Non-completers of the program gave mostly logistical and personal reasons for dropout. Conclusions: Overall, Run to Quit was well received by participants. Multiple health behaviour interventions at a scalable level appear feasible. Based on participant feedback, key recommendations to improve the program in the future include greater tailoring to walking or running preference, and increasing engagement with the Smokers Helpline.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".