Pills and prayers: a comparative qualitative study of community conceptualisations of pre-eclampsia and pluralistic care in Ethiopia, Haiti and Zimbabwe
Bibliographic record
Abstract
Pre-eclampsia is a leading cause of preventable maternal and perinatal deaths globally. While health inequities remain stark, removing financial or structural barriers to care does not necessarily improve uptake of life-saving treatment. Building on existing literature elaborating the sociocultural contexts that shape behaviours around pregnancy and childbirth can identify nuanced influences relating to pre-eclampsia care.We conducted a cross-cultural comparative study exploring lived experiences and understanding of pre-eclampsia in Ethiopia, Haiti and Zimbabwe. Our primary objective was to examine what local understandings of pre-eclampsia might be shared between these three under-resourced settings despite their considerable sociocultural differences. Between August 2018 and January 2020, we conducted 89 in-depth interviews with individuals and 17 focus group discussions (n = 106). We purposively sampled perinatal women, survivors of pre-eclampsia, families of deceased women, partners, older male and female decision-makers, traditional birth attendants, religious and traditional healers, community health workers and facility-based health professionals. Template analysis was conducted to facilitate cross-country comparison drawing on Social Learning Theory and the Health Belief Model.Survivors of pre-eclampsia spoke of their uncertainty regarding symptoms and diagnosis. A lack of shared language challenged coherence in interpretations of illness related to pre-eclampsia. Across settings, raised blood pressure in pregnancy was often attributed to psychosocial distress and dietary factors, and eclampsia linked to spiritual manifestations. Pluralistic care was driven by attribution of causes, social norms and expectations relating to alternative care and trust in biomedicine across all three settings. Divergence across the contexts centred around nuances in religious or traditional practices relating to maternal health and pregnancy.Engaging faith and traditional caregivers and the wider community offers opportunities to move towards coherent conceptualisations of pre-eclampsia, and hence greater access to potentially life-saving care.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".