Engaging community health workers in maternal and infant death identification in Khayelitsha, South Africa: a pilot study
Bibliographic record
Abstract
Abstract Background Engaging community health workers in a formalised death review process through verbal and social autopsy has been utilised in different settings to estimate the burden and causes of mortality, where civil registration and vital statistics systems are weak. This method has not been widely adopted. We piloted the use of trained community health workers (CHW) to investigate the extent of unreported maternal and infant deaths in Khayelitsha and explored requirements of such a programme and the role of CHWs in bridging gaps. Methods This was a mixed methods study, incorporating both qualitative and quantitative methods. Case identification and data collection were done by ten trained CHWs. Quantitative data were collected using a structured questionnaire. Qualitative data were collected using semi-structured interview guides for key informant interviews, focus group discussions and informal conversations. Qualitative data were analysed thematically using a content analysis approach. Results Although more than half of the infant deaths occurred in hospitals (n = 11/17), about a quarter that occurred at home (n = 4/17) were unreported. Main causes of deaths as perceived by family members of the deceased were related to uncertainty about the quality of care in the facilities, socio-cultural and economic contexts where people lived and individual factors. Most unreported deaths were further attributed to weak facility-community links and socio-cultural practices. Fragmented death reporting systems were perceived to influence the quality of the data and this impacted on the number of unreported deaths. Only two maternal deaths were identified in this pilot study. Conclusions CHWs can conduct verbal and social autopsy for maternal and infant deaths to complement formal vital registration systems. Capacity development, stakeholder’s engagement, supervision, and support are essential for a community-linked death review system. Policymakers and implementers should establish a functional relationship between community-linked reporting systems and the existing system as a starting point. There is a need for more studies to confirm or build on our pilot findings.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".