"You Can Collaborate and Go Together to Do Family Planning”: A Qualitative Endline Evaluation Study of Male Engagement in Promoting Reproductive, Maternal, Newborn and Child Health Services in Rural Kenya.
Bibliographic record
Abstract
Abstract BackgroundGlobally, male involvement in reproductive, maternal, newborn and child health (RMNCH) is associated with increased benefits for women, their children, and their communities. Between 2016 and 2020, the Aga Khan University has been implementing the Access to Quality of Care through Extending and Strengthening Health Systems (AQCESS), project funded by the Government of Canada and Aga Khan Foundation Canada (AKFC). A key component of the project was to encourage greater male engagement in RMNCH in rural Kisii and Kilifi, two predominantly patriarchal communities in Kenya, through a wide range of interventions. Towards the end of the project, we conducted a qualitative evaluation to explore how male engagement strategies influenced access to and utilization of RMNCH services.AIM This paper presents the endline evaluative study findings on how male engagement influenced reproductive, maternal, newborn and child health in rural Kisii and Kilifi.METHODSThe study used complementing qualitative methods in the AQCESS intervention areas. We conducted 10 focus group discussions with the community members across four groups including adult women, adult men, adolescent girls, and adolescent boys. We also conducted 11 key informant interviews with facility health managers,and sub county and county officials who were aware of the AQCESS project.FINDINGSMale engagement activities in Kisii and Kilifi counties were linked to improved knowledge and uptake of family planning, spousal/partner accompaniment to facility care and defeminization of social and gender roles.CONCLUSIONThis study supports the importance of male involvement in RMNCH in facilitating decisions on women and children’s health as well as in improving spousal support for use of family planning methods.
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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 | 0.025 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".