Quality of antenatal care and its potential impacts on delivery services and postnatal care compliance among reproductive women of Bangladesh
Bibliographic record
Abstract
Abstract Background: Ensuring quality antenatal care (ANC) and postnatal care (PNC) is crucial for reducing maternal and neonatal mortality rates. However, there are gaps in assessing the quality of ANC, leading to the proposal of standards by the World Health Organization. The study aims to examine the impact of quality ANC on delivery services and PNC compliance in Bangladesh using data from the Bangladesh Demographic and Health Survey (BDHS), providing insights for policymakers to improve maternal and neonatal health outcomes. Methods: This study used data from the 2017 Bangladesh Demographic and Health Survey (BDHS) to investigate the impact of quality antenatal care (qANC) on delivery services and PNC in Bangladesh. The study population included ever-married women aged 15-49 years who had experienced a recent pregnancy. The analysis assessed the relationship between qANC and facility delivery, skilled birth attendant (SBA)-assisted delivery, and PNC services within 48 hours of delivery. The study employed a two-stage stratified cluster sampling design, and data analysis was conducted using generalized linear models and considered various demographic and socioeconomic factors. Results: Key findings include a low rate of qANC services (18%), with pregnancy-related counseling being the lowest component. About 82% received at least one ANC visit, but only 18.3% received a quality visit. Higher compliance with facility delivery, SBA-conducted delivery, and PNC was observed when quality ANC was received. Factors such as completing secondary education, engaging in skilled/unskilled manual labor, higher wealth quintile, and birth parity of 1 were associated with better delivery and post-delivery outcomes. Conclusion: Ensuring qANC and expanding PNC service use remain challenging in Bangladesh. Increasing the provision of qANC is crucial as it is associated with higher adherence to PNC.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".