Community-based men’s health promotion programs: eight lessons learnt and their caveats
Bibliographic record
Abstract
Long-standing commentaries about men's reticence for accessing clinical medical services, along with the more recent recognition of men's health inequities, has driven work in community-based men's health promotion. Indeed, the 2000s have seen rapid growth in community-based programs targeting men, and across this expanse of innovative work, experiential and empirical insights afford some important lessons learnt, and caveats to guide existing and future efforts. The current article offers eight lessons learnt regarding the design, content, recruitment, delivery, evaluation and scaling of community-based men's health promotion programs. Design lessons include the need to address social determinants of health and men's health inequities, build activity-based programming, garner men's permission and affirmation to shift masculine norms, and integrate content to advance men's health literacy. Also detailed are lessons learnt about men-friendly spaces, recruitment and retention strategies, the need to incrementally execute program evaluations, and the limits for program sustainability and scaling. Drawing from diverse community-based programs to illustrate the lessons learnt, caveats are also detailed to contextualize and caution some aspects of the lessons that are shared. The express aim of discussing lessons learnt and their caveats, reflected in the purpose of the current article, is to guide existing and future work in the ever growing field of community-based men's health promotion.
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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 | 0.086 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".