Factors affecting deliveries attended by skilled birth attendants in Bangladesh
Bibliographic record
Abstract
The presence of skilled birth attendants (SBAs) is crucial in childbirth to reduce the maternal mortality ratio (MMR) and to achieve the maternal mortality target of the United Nations’ Sustainable Development Goals (SDGs). The aim of this study was to investigate the factors related to childbirths attended by SBAs in Bangladesh. Data from the Bangladesh Demographic and Health Survey (2014 BDHS) were analyzed. Logistic regression was applied to calculate crude odds ratios (CORs), adjusted odds ratios (AORs), 95% confidence intervals (CIs), and p-values. In Bangladesh, 35.9% of deliveries were attended by SBAs, and 44.2% of those women received at least one antenatal check-up by a skilled provider. The deliveries by SBAs were less than 50% of the total deliveries in all divisions, excluding Khulna. Known pregnancy complications (AOR: 1.2; 95% CI: 1.1–1.4), higher level of education in both women (AOR: 1.7; 95% CI: 1.2–2.3) and their husbands (AOR: 1.8; 95% CI: 1.3–2.4), receiving antenatal care (ANC) by a skilled provider during the pregnancy period (AOR: 1.5; 95% CI: 1.1–2.1), and higher wealth quintiles (AOR: 3.4; 95% CI: 2.5–4.7) were all significantly associated with an increased likelihood of a delivery by SBAs (p <0.05). In contrast, women living in rural areas (AOR: 0.7; 95% CI: 0.6–0.8) and the Sylhet Division (AOR: 0.4; 95% CI: 0.3–0.5) were less likely to be delivered by SBAs. To achieve the target of the Government of Bangladesh - 50% of deliveries to be attended by SBAs - it is important to increase ANC services and awareness programs in all seven divisions of Bangladesh. Special focus in rural areas is also required to meet this target. A new study should be conducted to explore the unexamined factors associated with the presence of SBAs during childbirth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".