Smoke-Free Men: Competing and Connecting to Quit
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to explore gender-related factors that motivate and support men's smoking reduction and cessation to inform effective men-centered interventions. Approach or Design: Focus group design using a semi-structured interview guide. SETTING: Three communities in British Columbia, Canada. PARTICIPANTS: A total of 56 men who currently smoked and were interested in reducing or quitting or had quit. INTERVENTION: N/A. METHODS: Data collected in 6 focus group discussions were transcribed and analyzed in accord with principles of thematic qualitative methods. RESULTS: We report the results across 4 interconnected themes: (1) the fight to quit takes several rounds, (2) the motivation of supportive competition, (3) challenges and benefits of connecting with smoke-free peers, and (4) playing up the physical and financial gains. CONCLUSIONS: Masculine-based perspectives positioned quitting alongside fighting for self-control, competing, connecting, physical prowess, and having extra cash as motivating components of programs to engage men in efforts to be smoke-free. It may be worthwhile to consider the inclusion of gain-framed and benefit-focused messaging in programs that support men's tobacco cessation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".