Narrative Review on the Trend of Childbirth in South Korea and Feasible Intervention to Reduce Cesarean Section Rate
Bibliographic record
Abstract
In this study, we explored the current childbirth trend in South Korea to provide recent evidence on determinants of the cesarean section rate (CSR) and related policy interventions. We utilized national health insurance claim data to analyze the CSR. We also conducted a narrative review on factors associated with the CSR and examined evidence about interventions to reduce it. The CSR is rising in Korea; simultaneously, the overall number of births is declining. In 2012, 469,000 women gave birth, and 26.9% underwent a cesarean section. In 2021, 249,000 women gave birth, and 58.7% experienced a cesarean section. The CSR among women under age 25 was 26.7% in 2012, but by the first quarter of 2022, it was 51.6%. In 2012, the CSR in women aged 25–34 years was 34.9%; by the first quarter of 2022, it was 58.3%. We synthesized evidence on the determinants of CSR in three dimensions: users, providers, and systems. We also explored recent evidence on policy interventions to reduce the CSR, focusing on women and families, providers, and hospitals. Despite the rapid increase in the CSR in the last decade, efforts to investigate childbirth choice and women’s experiences have been insufficient. We could not locate systematic initiatives in the research community or government to lower the rate. More patient-centered efforts to reduce the high CSR rate are needed.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".