If You Pay, Will They Come? Evaluating the Impact of Subsidies on Cessation Outcomes in the Walk or Run to Quit Program
Bibliographic record
Abstract
Introduction: Exercise interventions may assist smoking cessation attempts. One such publicly available 10-week program, Walk or Run to Quit (WRTQ), demonstrated success in smoking cessation and physical activity (PA) outcomes. However, initial WRTQ participants (2016-2017) were fairly homogenous in their demographic profile. To increase diversity, subsidies for participation were offered in 2018. This study assessed how the subsidies affected participant demographics, running frequency, smoking cessation, intention to quit, and program attendance and completion. Methods: The $70 registration fee was subsidized for 41% of participants in 2018. A pre-postdesign was used, with participants completing surveys on their demographics and smoking and physical activity behaviours. Descriptive statistics compared the year subsidies were available (2018) and unsubsidized years (2016-2017) and subsidized and unsubsidized participants' data from 2018. Results: The 2018 participants had lower average attendance and program completion rates compared to 2016-2017 and no statistically significant differences in demographics or smoking cessation and PA outcomes. There were no differences in smoking cessation, run frequency, or demographic variables between the subsidized and unsubsidized participants in 2018. Conclusions: Offering subsidies did not diversify the participant profile. Subsidies did not have a negative impact on attendance nor primary outcomes. Subsidies may not have addressed barriers that prevented a more diverse sample from participating in WRTQ, such as program location, timing, and design. Equitable access to smoking cessation programs remains essential. As subsidies may play a role in reducing financial barriers disproportionately faced by marginalized groups, the implementation of, and recruitment for, such subsidized programs requires further investigation.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".