CAESAREAN SECTION INCIDENTS AND COST IN WEST SUMATRA: A COMPARATIVE STUDY BETWEEN PRIVATE AND PUBLIC HOSPITALS UNDER INA CBGS SYSTEM
Bibliographic record
Abstract
Since its first launched in 2014, many hospitals claimed to have financial loss due to inequality between hospital real tariff and BPJS payment. It is important to identify the characteristics and trends of caesarean deliveries in hospitals prior to measuring the efficiency level under INA-CBGs system. However, until now there is yet to be a descriptive study to understand this issue comprehensively. To fill this research gap, this study aimed to compare caesarean section incidents and costs between private and public hospitals in West Sumatra, Indonesia. This study is expected to describe real situation related to caesarean cases and explore the preference of patients in the province for caesarean deliveries. This study was retrospective and cross-sectional design of caesarean section in all West Sumatran hospitals under INA-CBGs system. The data used was taken from BPJS region West Sumatra-Riau-Jambi for period 2016 to 2018. During that period, almost 59 thousand caesarean section were performed, of which 64% in private hospitals and 36% in public hospitals. Within three years, caesarean cases in private hospitals were almost doubled while public hospitals showed a decreasing trend. In 2018, three quarter of caesarean costs was paid by BPJS to private hospitals while public hospitals only received a quarter of total costs.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".