"I'm a runner not a smoker": Change in identity as a predictor of success in the run to quit program
Bibliographic record
Abstract
This study examined the influence of identity on activity and smoking within Run to Quit, a national multi-behaviour intervention that utilizes group-based learn-to-run clinics to assist participants in quitting smoking. It was hypothesized that the behaviour-change curriculum and running, especially in a group context, would not only help participants with physical symptoms of withdrawal but it would also facilitate changes in identity. Further, changes in identity were hypothesized to predict cessation and running in participants. Inactive adult smokers (n=249) completed 10-week clinics in Running Room locations across Canada. Within the mixed-methods pre-post design, participants completed questionnaires assessing identity, physical activity, running frequency, and smoking status (including carbon monoxide [CO] testing) at weeks 1 and 10, as well as post-program interviews. Consistent with hypotheses, in addition to significant changes in smoking status and activity, runner identity increased, and smoker identity decreased from beginning to end of program (p's=.000). Further, two regression analyses were significant with changes in smoker identity being the significant predictor of CO levels (? =.302, p=.000) and changes in run identity best predicting running frequency (? =.235, p=.001). While runner identity was correlated to quit outcomes and smoker identity correlated to running (p's=.000), they were not significant predictors in their respective models, and neither was the interaction between the two identities. Although future experimental research is required, these findings, along with complimentary qualitative feedback, suggest changes in identity may be one potential mechanism through which physical activity facilitates smoking cessation.Acknowledgments: The authors would like to sincerely thank the staff and management at Running Room Canada and the Canadian Cancer Society for providing the opportunity to collaborate on this project. This work was funded by the Public Health Agency of Canada.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".