Gender and health social enterprises in Africa: a research agenda
Bibliographic record
Abstract
BACKGROUND: Health social enterprises in Africa working with community health workers (CHWs) are growing rapidly but understudied. In particular, gender equality issues related to their work has important public health and equity implications. METHODS: Particularly suited for generating timely findings from reviews at the intersection of overlapping disciplines, we utilized the rapid evidence assessment (REA) methodology to identify key unanswered research questions at the intersection of the fields of gender equality, social enterprises and community health workers. The REA used a series of structured Google Scholar searches, expert interviews and bibliography reviews to identify 57 articles in the academic and grey literatures that met the study inclusion criteria. Articles were thematically coded to identify answers to "What are the most important research questions about the influence of gender on CHWs working with health social enterprises in Africa?" RESULTS: The analysis identified six key unanswered research questions relating to 1) equitable systems and structures; 2) training; 3) leadership development and career enhancement; 4) payment and incentives; 5) partner, household and community support; and 6) performance. CONCLUSION: This is the first study of its kind to identify the key unanswered research questions relevant to gender equality in health social enterprises in Africa using community health workers. As such, it sets out a research agenda for this newly emerging but rapidly developing area of research and practice with important public health implications.
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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".