Exploring the impact of a community participatory intervention on women's capability: a qualitative study in Gulu Northern Uganda
Bibliographic record
Abstract
BACKGROUND: Community participatory interventions mobilizing women of childbearing age are an effective strategy to promote maternal and child health. In 2017, we implemented this strategy in Gulu Northern Uganda. This study explored the perceived impact of this approach on women's capability. METHODS: We conducted a qualitative study based on three data collection methods: 14 in-depth individual interviews with participating women of childbearing age, five focus group discussions with female facilitators, and document analysis. We used the Sen capability approach as a conceptual framework and undertook a thematic analysis. RESULTS: Women adopted safe and healthy behaviors for themselves and their children. They were also able to respond to some of their family's financial needs. They reported a reduction in domestic violence and in mistreatment towards their children. The facilitators perceived improved communication skills, networking, self-confidence, and an increase in their social status. Nevertheless, the women still faced unfreedoms that deprived them of living the life they wanted to lead. These unfreedoms are related to their lack of access to economic opportunities and socio-cultural norms underlying gender inequalities. CONCLUSION: To expand women's freedoms, we need more collective political actions to tackle gender inequalities and need to question the values underlying women's social status.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".