Male Involvement in Reproductive and Maternal and New Child Health: An Evaluative Qualitative Study on Facilitators and Barriers From Rural Kenya
Bibliographic record
Abstract
Male involvement in reproductive, maternal, newborn and child health (RMNCH) is known to improve maternal and child health outcomes. However, there is sub-optimal adoption of male involvement strategies in several low- and middle-income countries such as Kenya. Aga Khan University implemented Access to Quality of Care through Extending and Strengthening Health Systems (AQCESS), a project funded by the Government of Canada and Aga Khan Foundation Canada (AKFC), between 2016 and 2020 in rural Kisii and Kilifi counties, Kenya. A central element in the interventions was increasing male engagement in RMNCH. Between January and March 2020, we conducted an endline qualitative study to examine the perspectives of different community stakeholders, who were aware of the AQCESS project, on the facilitators and barriers to male involvement in RMNCH. We found that targeted information sessions for men on RMNCH are a major facilitator to effective male engagement, particularly when delivered by male authority figures such as church leaders, male champions and teachers. Sub-optimal male engagement arises from tensions men face in directly contributing to the household economy and participating in RMNCH activities. Social-cultural factors such as the feminization of RMNCH and the associated stigma that non-conforming men experience also discourage male engagement.
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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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".