Designing Men’s Health Programs: The 5C Framework
Bibliographic record
Abstract
Men are less likely than women to access or engage with a range of generic health programs across a diversity of settings. Designing health programs that mitigate barriers associated with normative ideals of masculinity has been widely viewed as a key factor in how health systems should respond, but strategies to engage men have often narrowly conceptualized male health behavior and risk inadvertently reinforcing negative and outdated gender stereotypes. Currently absent from the men's health literature is practical guidance on gender-transformative approaches to men's health program design-those which seek to quell harmful gender norms and purposefully promote health equity across wide-ranging issues, intervention types, and service contexts. In this article, we propose a novel conceptual model underpinned by gender-transformative goals to help guide researchers and practitioners tailor men's health programs to improve accessibility and engagement. The "5C framework" offers key considerations and guiding principles on the application of masculinities in program design irrespective of intervention type or service context. By detailing five salient phases of program development, the framework is intended as a designate approach to the design of accessible and engaging men's health programs that will foster progressive changes in the ways in which masculinity can be interpreted and expressed as a means to achieve health for all.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".