Barriers and enablers to engaging rural men in chronic disease prevention and management programs
Bibliographic record
Abstract
Canadian men, especially those living in rural/remote regions, are at risk for a number of lifestyle-related chronic diseases (CD) including type 2 diabetes, high blood pressure, heart disease, stroke and some forms of cancer. CDs share many risk factors including overweight, physical inactivity, unhealthy diet, and smoking. Poor health outcomes suggest that Canadian men continue to fall short in adopting healthy lifestyles that could prevent CD. There are also alarming discrepancies in CD burden by geography, whereby rural residence increases the risk of developing CD and results in poorer health outcomes. The community-based HealtheStepsTM program offers lifestyle prescriptions (exercise, healthy eating), coaching, and technology supports to help Canadians become more physically active, eat better, and improve their health. The HealtheStepsTM program has been implemented in a variety of community settings, and more than 90% of the participants have been women. In addition, our community partners have reported challenges with regard to engaging men in group and individual health programs. The proposed presentation will cover two related studies conducted by the HealtheStepsTM research team that explored factors influencing the participation of rural men in CD prevention and management (CDPM) programs. In the first study, 149 men living in SW Ontario completed a survey that measured health behaviours and perceptions of CDPM programs. The second study was a scoping review of academic and grey literature related to factors influencing the engagement of rural men in CDPM programs. Prominent themes that were uncovered in both studies include negative perceptions of group-based programs and the importance of considering psychosocial (e.g., the desire to appear masculine) and program-specific factors (e.g., program characteristics) when designing male-specific CDPM programs. Conflicting results between our survey findings and the literature review will also be discussed.Acknowledgments: The authors would like to acknowledge the Canadian Institutes of Health Research for funding this project, titled HealtheSteps: Strategies to Engage Rural Canadian Men in Chronic Disease Prevention and Management Programs (#129593)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".