Gender Intentional Approaches to Enhance Health Social Enterprises in Africa: A Qualitative Study of Constraints and Strategies
Bibliographic record
Abstract
Abstract Background: Health social enterprises are experimenting with community health worker (CHW) models that allow for various income-generating opportunities to motivate and incentivize CHWs. Although evidence shows that improving gender equality contributes to the achievement of health outcomes, gender-based constraints faced by CHWs working with social enterprises in Africa have not yet been empirically studied. This study is the first of its kind to address this important gap in knowledge. Methods: We conducted 30 key informant interviews and 21 focus group discussions between 2016 and 2020 (for a total of 175 individuals: 106 women and 69 men) with four health social enterprises in Uganda and Kenya and other related key stakeholders and domain experts. Interview and focus group transcripts were coded according to gender-based constraints and strategies for enhanced performance as well as key sites for intervention. Results: We found that CHW programs can be more gender responsive. We introduce the Gender Integration Continuum for Health Social Enterprises as a tool that can help guide gender equality efforts. Data revealed female CHWs face seven unique gender-based constraints (compared to male CHWs): higher time burden and lack of economic empowerment; risks to personal safety; lack of career advancement and leadership opportunities; lack of access to needed equipment, medicines and transport; lack of access to capital; lack of access to social support and networking opportunities; and insufficient financial and non-financial incentives. Data also revealed four key areas of intervention: the health social enterprise, the CHW, the CHW’s partner, and the CHW’s patients. In each of the four areas, gender responsive strategies were identified to overcome constraints and contribute to improved gender equality and community health outcomes. Conclusions: This is the first study of its kind to identify the key gender-based constraints and gender responsive strategies for health social enterprises in Africa using CHWs. Findings can assist organizations working with CHWs in Africa (social enterprises, governments or non-governmental organizations) to develop gender responsive strategies that increase the gender and health outcomes while improving gender equality for CHWs, their families, and their communities.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.009 |
| 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".