Modelling the First Two Delays of the "Three-Delays Model" for Emergency Obstetric Care in Bangladesh: A Choice Model Approach
Bibliographic record
Abstract
Women in Bangladesh have a life-time risk of dying from pregnancy and child-related causes which is estimated to be about 500 times higher than that in the developed countries. More than 100 mothers die daily in Bangladesh due to maternity-related causes. Studies show that 5% of about 600,000 patients in Bangladesh with obstetric complications attend medical facilities, 27.5% of pregnant women receive some prenatal care, 3.5% of women deliver in institutions, and practically no women go for postnatal care. The provision of emergency obstetric care is an extremely important element in the prevention of maternal mortality. While there are inadequate emergency obstetric care services in Bangladesh, those which are available are underutilized because of the medical, social, cultural, economic, geographic, and community barriers women with obstetric complications encounter. The "three-delays" model proposed by Maine in 1993 depicts the roles of communities and the health system in the use status of emergency obstetric care. The model suggests that the outcome of an obstetric emergency is influenced by factors which govern the decision to seek care, reaching the medical facility, and receiving adequate treatment. These three delays impede the use of emergency obstetric care facilities. Using an econometric choice model, the authors identify the relative importance of the factors which affect the first two delays.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".