Repositioning traditional birth attendants to provide improved maternal healthcare services in rural Ghana
Bibliographic record
Abstract
Following the World Health Organization's recommendation for developing countries to discontinue the use of Traditional Birth Attendants (TBAs) in rural areas, the government of Ghana banned TBAs from offering maternal health care services. Since this ban, community-level conflicts have intensified between TBAs, (who still see themselves as legitimate culturally mandated traditional midwives) and nurses. In this articles, we propose a partnership model for a sustainable resolution of these conflicts. This article emanates from the apparent ideological discontent between people from mainstream medical practice who advocate for the complete elimination of TBAs in the maternal health service space and individuals who argue for the inclusion of TBAs in the health sector given the shortage of skilled birth attendants and continued patronage of their services by rural women even in context where nurses are available. In the context of the longstanding manpower deficit in the health sector in Ghana, improving maternal healthcare in rural communities will require harnessing all locally available human resources. This cannot be achieved by "throwing out" a critical group of actors who have been involved in health-care provision for many decades. We propose a win-win approach that involve retraining of TBAs, partnership with health practitioners, and task shifting.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".