Community Members’ Perspectives on Men’s Risk and Protective Health Factors: A Community-Based Participatory Research Study
Bibliographic record
Abstract
Despite men’s health playing a significant role in the well-being of infants, children, and women, there is a gap in maternal and child health research which more broadly considers men’s health as a component of family’s and community’s overall well-being and for the sake of men’s own health and well-being, particularly from the perspective of men with marginalized identities, such as Black men, and community members. Therefore, our community-based participatory research study aimed to explore what community members perceive as protective and risk factors for the general health of men in a low-income community using a generic qualitative approach with focus groups and thematic analysis. We identified six protective factor themes (health behaviors, economic stability, expected male responsibilities, healthcare engagement, social network, and faith, spirituality, and driving forces), as well as six risk factor themes (health behaviors, impact of mentorship, experience of driving forces, healthcare avoidance, mental health concerns, and systemic bias, racism, and social inequity). There are actionable steps public health practitioners and policymakers should prioritize, including addressing structural barriers to men’s health, such as by combating discrimination and increasing access to healthcare, removing barriers to mental health care, and creating opportunities for increased social support. These strategies can give way to greater opportunities for men to engage in protective behaviors that can both improve their health across the life-course and positively impact the health of mothers, infants, children, and communities.
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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 | 0.055 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".