Evaluating the Outcomes of a Quit and Win Contest Among Young Adults Enrolled in Post-Secondary Schools and Not in Post-Secondary Schools
Bibliographic record
Abstract
Abstract Objective: This study compares Quit and Win contest outcomes for young adults enrolled in post-secondary schools and young adults not enrolled in post-secondary schools. Participants: Of the 4,299 18-to-29-year-olds who enrolled in the 2019 Wouldurather... contest to quit smoking and agreed to participate in the study. 535 (12.4%) were retained in the final sample: 207 were attending post-secondary schools and 328 were not attending post-secondary schools. Methods: Participants answered baseline questions addressing demographics and smoking/quitting behaviours and intentions. Six weeks after the start of the contest, participants completed an Intervention Check assessing use of contest supports (emails, Facebook group), perceived value of the prize, and use of quit aids. Abstinence outcomes were assessed 3 months after the start of the contest. Results: At follow-up, 21.9% of participants reported 3 months of total abstinence from smoking, with no difference between those attending and not attending post-secondary schools Χ2(1, 533) = 0.9. Confidence to remain smoke free increased significantly over time F(1, 115) = 32.2, p < .01The prize was highly-valued; use of contest supports was moderate. Adjusted logistic regression revealed abstinence was not associated with contest supports or valuing the prize. Conclusion: Community and campus health professionals should consider pooling their resources to offer all young adults a single contest with a large prize. Dose-response relationships of contest supports to quitting should be explored.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".