Impact of Provider Competition under Global Budgeting on the Use of Cesarean Delivery
Bibliographic record
Abstract
OBJECTIVE: To examine the impact of provider competition under global budgeting on the use of cesarean delivery in Taiwan. DATA SOURCES/STUDY SETTING: (1) Quarterly inpatient claims data of all clinics and hospitals with birth-related expenses from 2000 to 2008; (2) file of health facilities' basic characteristics; and (3) regional quarterly point values (price conversion index) for clinics and hospitals, respectively, from the fourth quarter in 1999 to the third quarter in 2008, from the Statistics of the National Health Insurance Administration. STUDY DESIGN: Panel data of quarterly facility-level cesarean delivery rates with provider characteristics, birth volumes, and regional point values are analyzed with the fractional response model to examine the effect of external price changes on provider behavior in birth delivery services. PRINCIPAL FINDINGS: The decline in de facto prices of health services as a result of noncooperative competition under global budgeting is associated with an increase in cesarean delivery rates, with a high degree of response heterogeneity across different types of provider facilities. CONCLUSIONS: While global budgeting is an effective cost containment tool, intensified financial pressures may lead to unintended consequences of compromised quality due to a shift in provider practice in pursuit of financial rewards.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".