Barriers to Recruiting Men Into Chronic Disease Prevention and Management Programs in Rural Areas
Bibliographic record
Abstract
Chronic disease is becoming increasingly prevalent in Canada. Many of these diseases could be prevented by adoption of healthy lifestyle habits including physical activity and healthy eating. Men, especially those in rural areas, are disproportionately affected by chronic disease. However, men are often underrepresented in community-based chronic disease prevention and management (CDPM) programs, including those that focus on physical activity and/or healthy eating. The purpose of this study was to explore the experiences and perceptions of program delivery staff regarding the challenges in recruitment and participation of men in physical activity and healthy eating programs in rural communities, and suggestions for improvement. Semistructured interviews were conducted by telephone with 10 CDPM program delivery staff from rural communities in Southwest Ontario, Canada. Time and travel constraints, relying on spouses, and lack of male program leaders were cited as barriers that contributed to low participation levels by men in CDPM programs. Hiring qualified male instructors and engaging spouses were offered as strategies to increase men's participation. The results of this study highlight many of the current issues faced by rural health organizations when offering CDPM programming to men. Health care organizations and program delivery staff can use the recommendations in this report to improve male participation levels.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| 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".