Quality of care during childbirth at public health facilities in Bangladesh: a cross-sectional study using WHO/UNICEF ‘Every Mother Every Newborn (EMEN)’ standards
Bibliographic record
Abstract
BACKGROUND: This manuscript presents findings from a baseline assessment of health facilities in Bangladesh prior to the implementation of the 'Every Mother Every Newborn Quality Improvement' initiative. METHODOLOGY: A cross-sectional survey was conducted between June and August 2016 in 15 government health facilities. Structural readiness was assessed by observing the physical environment, the availability of essential drugs and equipment, and the functionality of the referral system. Structured interviews were conducted with care providers and facility managers on human resource availability and training in the maternal and newborn care. Observation of births, reviews of patient records and exit interviews with women who were discharged from the selected health facilities were used to assess the provision and experience of care. RESULTS: Only six (40%) facilities assessed had designated maternity wards and 11 had newborn care corners. There were stock-outs of emergency drugs including magnesium sulfate and oxytocin in nearly all facilities. Two-thirds of the positions for medical officers was vacant in district hospitals and half of the positions for nurses was vacant in subdistrict facilities. Only 60 (45%) healthcare providers interviewed received training on newborn complication management. No health facility used partograph for labour monitoring. Blood pressure was not measured in half (48%) and urine protein in 99% of pregnant women. Only 27% of babies were placed skin to skin with their mothers. Most mothers (97%) said that they were satisfied with the care received, however, only 46% intended on returning to the same facility for future deliveries. CONCLUSIONS: Systematic implementation of quality standards to mitigate these gaps in service readiness, provision and experience of care is the next step to accelerate the country's progress in reducing the maternal and neonatal deaths.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".