Practices and determinants of delivery by skilled birth attendants in Bangladesh
Bibliographic record
Abstract
INTRODUCTION: Utilization of Skilled Birth Attendants (SBAs) at birth is low (20%) in Bangladesh. Birth attendance by SBAs is considered as the "single most important factor in preventing maternal deaths". This paper examined the practices and determinants of delivery by SBAs in rural Bangladesh. METHODS: The data come from the post-intervention survey of a cluster-randomized community controlled trial conducted to evaluate the impact of limited post-natal care (PNC) services on healthcare seeking behavior of women with a recent live birth in rural Bangladesh (n = 702). Multivariable logistic regression model was used to identify the potential determinants of delivery by SBAs. RESULTS: The respondents were aged between 16 and 45, with the mean age of 24.41 (± 5.03) years. Approximately one-third (30.06%) of the women had their last delivery by SBAs. Maternal occupation, parity, complications during pregnancy and antenatal checkup (ANC) by SBAs were the significant determinants of delivery by SBAs. Women who took antenatal care by SBAs were 2.62 times as likely (95% CI: 1.66, 4.14; p < 0.001) to have their delivery conducted by SBAs compared to those who did not, after adjusting for other covariates. CONCLUSION: Our findings suggest that ANC by SBAs and complications during pregnancies are significant determinants of delivery by SBAs. Measure should be in place to promote antenatal checkup by SBAs to increase utilization of SBAs at birth in line with achieving the Millennium Development Goal-5. Future research should focus in exploring the unmet need for, and potential barriers in, the utilization of delivery by SBAs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".