Factors influencing delivery-related complications and their consequences in hard-to-reach areas of Bangladesh
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Bangladesh's high maternal mortality ratio is exacerbated by delivery-related complications, particularly in hard-to-reach (HtR) areas with limited healthcare access. Despite this, few studies have explored delivery-related complications and factors contributing to these complications among the disadvantaged population. This study aimed to investigate the factors contributing to delivery-related complications and their consequences among the mothers residing in the HtR areas of Bangladesh. METHODS: Data were collected using a cross-sectional study design from 13 HtR sub-districts of Bangladesh between September 2019 and October 2019. Data from 1,290 recently delivered mothers were analysed. RESULTS: Around 32% (95% CI: 29.7-34.8) of the mothers reported at least one delivery-related complication. Prolonged labour pain (21%) was the highest reported complication during the delivery, followed by obstructive labour (20%), fever (14%), severe headache (14%). Mothers with higher education, a higher number of antenatal care (ANC) visits, complications during ANC, employed, and first-time mothers had higher odds of reporting delivery-related complications. More than one-half (51%) of these mothers had normal vaginal delivery. Nearly one-fifth (20%) of mothers who reported delivery-related complications were delivered by unskilled health workers at homes. On the other hand, about one-fifth (19%) of the mothers without any complications during delivery had a caesarean delivery. Nine out of ten of these caesarean deliveries were done at the private facilities. CONCLUSION: Delivery-related complications are significantly related to a woman's reproductive history and other background characteristics. Unnecessary caesarean delivery is prominent at private facilities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".