Health system factors and caesarean sections in Kosovo: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: To investigate the association of caesarean section rates with the health system characteristics in the public hospitals of Kosovo. DESIGN: Cross-sectional survey. SETTING: Five largest public hospitals in Kosovo. PARTICIPANTS: 859 women with low-risk deliveries who delivered from April to May 2015 in five public hospitals in Kosovo. OUTCOME MEASURES: The prespecified outcomes were the crude and adjusted OR of births delivered with caesarean section by health system characteristics such as delivery by the physician who provided antenatal care, health insurance status and other. Additional prespecified outcomes were caesarean section rates and crude ORs for delivery with caesarean in each public hospital. RESULTS: Women with personal monthly income had increased odds for caesarean (OR 1.55, 95% CI 1.06 to 2.27), as did women with private health insurance coverage (OR 3.44, 95% CI 1.20 to 9.85). Women instructed by a midwife on preparation for delivery had decreasing odds (OR 0.32, 95% CI 0.19 to 0.51) while women having preference for a caesarean had increasing odds for delivery with caesarean (OR 3.84, 95% CI 1.96 to 7.51). The odds for caesarean increased also in the case of delivery by a physician who provided antenatal care (OR 2.06, 95% CI 1.16 to 3.67) and delivery during office hours (OR 2.36, 95% CI 1.37 to 4.05), while delivery at the University Clinical Centre of Kosovo decreased the odds for caesarean (OR 0.46, 95% CI 0.24 to 0.90). CONCLUSIONS: We found that several health system characteristics are associated with the increase of caesarean sections in a low-risk population of delivering women in public hospitals of Kosovo. These findings should be explored further and addressed via policy measures that would tackle provision of unnecessary caesareans. The study findings could assist Kosovo to develop corrective policies in addressing overuse of caesareans and may provide useful information for other middle-income countries.
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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 | 0.001 | 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".