Changing scenario of C-section delivery in India
Bibliographic record
Abstract
BACKGROUND: Caesarean section (C-section) delivery is a serious maternal health concern in the long run. Notedly, there is a lack of studies dealing with understanding the ways and reasons of C-section deliveries becoming a public health issue in today's time in India and the measures to reduce the unnecessary caesarean sections. We have conducted this study to study the changes in the state-wise prevalence of C-section deliveries in India and understand C-section delivery's socioeconomic and biomedical predictors. MATERIALS AND METHODS: The study uses data from the fourth and fifth rounds of the National Family Health Surveys (NFHS). The per cent differences in the C-section deliveries from NFHS-4 to NFHS-5 across the states were measured through relative changes. The association between the C-section delivery and socioeconomic and biomedical factors were assessed using multiple logistic regression. RESULTS: This study revealed that the C-section deliveries are higher in the southern states than in the other parts of India. Literacy plays a vital role in C-section deliveries. The probabilities of C-section deliveries are more in 30-40 and 40 + years. The women belonging to the median wealth index category were more likely (OR-CI, 1.62 [1.55-1.66]) to undergo the C-section followed by the women from wealthy households (OR-CI, 1.46 [1.41-1.52]). CONCLUSION: The Government's health policymakers should take the initiative to reduce the C-section section delivery by means of building maternal health literacy and awareness among women and the community so that its future implications can be minimised. It is crucial to formulate a mandate and implement it in the states where C-sections are too high through community health workers and primary care providers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".