Designing Gender-Responsive Health Promotion Programs for Men: A Scoping Review
Bibliographic record
Abstract
Over the past decade, there has been an increased emphasis on tailoring men's health promotion programs. To optimize outcomes, participatory action research that involves and elicits feedback from end-users has been highlighted as important to creating gender-responsive interventions. In this scoping review, we examine (a) how participatory action research has been used to design health promotion interventions for men and (b) what constitutes a gender-responsive intervention design. Following a comprehensive search, 53 articles were included in the review, reporting on 35 men's health promotion programs. Our findings suggest that participatory action methods harness varying degrees of end-user involvement, with a large majority limited to post-intervention evaluations rather than co-design and consumer collaboration. In addition, there are inconsistencies for applying gender-responsive approaches within programs, particularly regarding how interventions are targeted, tailored, and promoted to men. We conclude that participatory action research methods translate to varying degrees of gender responsiveness in men's health promotion programs. That said, involving end-users at various stages of intervention design, implementation, and evaluation may increase the likelihood that programs are more attuned to masculinities and better engage participants in promoting healthy behavior change. Efforts to advance gender-responsive designs can benefit from inductively deriving and incorporating men's masculine values.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".