A qualitative study to explore the attitudes of women and obstetricians towards caesarean delivery in rural Bangladesh
Bibliographic record
Abstract
BACKGROUND: Caesarean section is a lifesaving surgical intervention for women and their newborns, though overutilization is a public health concern. The caesarean rate in Bangladesh is approximately 23% overall, and in private facilities it is over 70%. It is essential to know both the supply side (obstetricians) and demand side (parturient women) views on caesarean birth in order to formulate specific interventions to address the escalating rate of caesareans. METHODS: This qualitative study took place in Matlab, a rural sub-district in Bangladesh. We interviewed women attending their 3rd antenatal visit, those with recent caesareans, and obstetricians from both public and private health facilities. In total there were twenty in-depth interviews and four focus group discussions. Study participants were asked about their preferences on birthing mode and knowledge of the caesarean section process. Thematic data analysis was done following a deductive approach. RESULTS: Women from this rural community had a strong preference for normal vaginal birth. However, they were willing to accept the attending health care provider's decision for caesarean birth. Antenatal care sessions did not provide information on the medical indications for caesarean section. Furthermore, some women had the misconception that episiotomy itself is a 'small caesarean.' Primary health care providers and clinic agents (brokers) had a strong influence on women's decision to choose a health facility for giving birth. However, obstetricians, having a preference for caesarean section, were receiving more patients from these brokers which may be an important reason for the high rate of clinically non-indicated caesareans at private hospitals in Bangladesh. Improper labour monitoring and inadequate staffing at health facilities were additional influences on the preference for caesarean section. However, critical knowledge gaps were also observed among study obstetricians, particularly with regards to the indications for and timing of elective caesarean sections. CONCLUSION: There is a need to educate women about the advantages and disadvantages of different birthing modes to ensure their active participation in the decision making process. Strong policy regulations are needed to ensure legitimate decision making by obstetricians regarding mode of birthing.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".