Leaving no one behind: using action research to promote male involvement in maternal and child health in Iringa region, Tanzania
Bibliographic record
Abstract
INTRODUCTION: Male involvement has been reported to improve maternal and child health (MCH) outcomes. However, most studies in low-income and middle-income countries have reported low participation of men in MCH-related programmes. While there is a growing interest in the involvement of men in MCH, little is known on how male involvement can be effectively promoted in settings where entrenched unequal gender roles, norms and relations constrain women from effectively inviting men to participate in MCH. METHODS AND ANALYSIS: This paper reports participatory action research (PAR) aimed to promote male participation in pregnancy and childbirth in Iringa Region, Tanzania. As part of the Innovating for Maternal and Child Health in Africa project, PAR was conducted in 20 villages in two rural districts in Tanzania. Men and women were engaged separately to identify barriers to male involvement in antenatal care and during delivery; and then they were facilitated to design strategies to promote male participation in their communities. Along with the PAR intervention, researchers undertook a series of research activities. A thematic analysis was used to analyse the data. The common strategies designed were: engaging health facility committees; using male champions and male gatekeepers; and using female champions to sensitise and provide health education to women. These strategies were validated during stakeholders' meetings, which were convened in each community. DISCUSSION: The use of participatory approach not only empowers communities to diagnose barriers to male involvement and develop culturally acceptable strategies but also increases sustainability of the interventions beyond the life span of the project. More lessons will be identified during the implementation of these strategies.
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 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 | 0.032 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".