Promoting gender equity in a home visits programme: a qualitative study in Northern Nigeria
Bibliographic record
Abstract
BACKGROUND: Gender inequities remain critical determinants influencing maternal health. Harmful gender norms and gender-based violence adversely affect maternal health. Gendered division of labour, lack of access to and control of resources, and limited women's decision-making autonomy impede women's access to maternal healthcare services. We undertook a cluster randomized controlled trial of universal home visits to pregnant women and their spouses in one local government area in Bauchi State, North-Eastern Nigeria. The trial demonstrated a significant improvement in maternal and child health outcomes and male knowledge, attitudes and behaviours. This paper qualitatively evaluates gender equity in the home visits programme. METHODS: The research team explored participants' views about gender equity in the home visits programme. We conducted nine key informant interviews with policymakers and 14 gender and age-stratified focus group discussions with men and women from visited households, with women and men home visitors and supervisors, and with men and women community leaders. Analysis used an adapted conceptual framework exploring gender equity in mainstream health. A deductive thematic analysis of interviews and focus group reports looked for patterns and meanings. RESULTS: All respondents considered the home visits programme to have a positive impact on gender equity, as they perceived gender equity. Visited women and men and home visitors reported increased male support for household chores, with men doing heavy work traditionally pre-assigned to women. Men increased their support for women's maternal health by paying for healthcare and providing nutritious food. Households and community members confirmed that women no longer needed their spouses' permission to use health services for their own healthcare. Households and home visitors reported an improvement in spousal communication. They perceived a significant reduction in domestic violence, which they attributed to the changing attitudes of both women and men due to the home visits. All stakeholder groups stressed the importance of engaging male spouses in the home visits programme. CONCLUSION: The home visits programme, as implemented, contributed to gender equity.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 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) | 0.003 | 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".