A Qualitative Endline Evaluation Study of Male Engagement in Promoting Reproductive, Maternal, Newborn, and Child Health Services in Rural Kenya
Bibliographic record
Abstract
Background: Globally, 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 implemented 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. Toward the end of the project, we conducted a qualitative evaluation to explore how male engagement strategies influenced access to and utilization of RMNCH services. This paper presents the endline evaluative study findings on how male engagement influenced RMNCH in rural Kisii and Kilifi. Methods: The study used complementing qualitative methods in the AQCESS intervention areas. We conducted 10 focus group discussions (FGDs) with 82 community members across four groups including adult women, adult men, adolescent girls, and adolescent boys. We also conducted 11 key informant interviews (KIIs) with facility health managers, and sub-county and county officials who were aware of the AQCESS project. Results: Male engagement activities in Kisii and Kilifi counties were linked to improved knowledge and uptake of family planning (FP), spousal/partner accompaniment to facility care, and defeminization of social and gender roles. Conclusion: This 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 FP methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".