Deliver in the right place and get there in time! Healthcare-seeking behaviour for delivery in cases of stillbirths and neonatal deaths in rural Cambodia: a prospective cohort social autopsy study
Bibliographic record
Abstract
Abstract Introduction Perinatal mortality remains high in low-resource settings, with many deaths preventable. While clinical causes of stillbirths and neonatal deaths are often examined, non-medical factors such as care-seeking behaviour and barriers are less well understood. This study uses social autopsy to explore upstream, social factors associated with stillbirths and neonatal deaths in rural Cambodia. Methods A prospective, population-based observational study over three years (2019–2022) in Preah Vihear province, Cambodia. A social autopsy questionnaire to examine socio-demographic characteristics, health-seeking behaviours, and delays in healthcare-seeking was developed. The Three Delays model was used to summarise barriers faced by pregnant women at the time of delivery. Social autopsy interviews were conducted for all stillbirths and neonatal deaths. Data were analysed descriptively. Results Social autopsy was completed for 315 out of 404 (78.0%) stillbirths and neonatal deaths. Most mothers (87.3%, 275/315) reached a health facility for delivery. However, 20.4% (56/275) of them bypassed their nearest facility. Among women attending their nearest facility, 64.8% (142/219) delivered there, and of these, 69.0% (98/142) delivered within one hour of arrival. In total, nearly half of all deliveries (49.5%) occurred either at home (28/315), enroute (12/315), or within one hour of arrival at the first facility (116/315). Delays to seeking facility-based care for delivery were common: 65.7% (207/315) of women reported experiencing at least one delay type, most often at home (43.5%) or at the facility (36.7%). Conclusion Healthcare-seeking behaviour was generally appropriate, but not timely. Deliveries occurring very soon after arrival at health facilities likely limited the quality of care that healthcare workers could provide. Addressing home and facility delays may help to reduce these late-presenting deliveries, as well as reducing non-facility births. To improve the timeliness of facility arrival by pregnant women for delivery, we need to better understand the perspectives of families and healthcare workers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".