Improved obstetric management after implementation of a scaled‐up quality improvement intervention: A nested before‐after study in three public hospitals in Nepal
Bibliographic record
Abstract
BACKGROUND: We assessed the change in obstetric management after implementation of a quality improvement intervention, the Nepal Perinatal Quality Improvement Package (NePeriQIP). METHODS: The Nepal Perinatal Quality Improvement Package was a stepped-wedge cluster-randomized controlled trial conducted in 12 public hospitals in Nepal between April 2017 and October 2018. In this study, three hospitals allocated at different time points to the intervention were selected for a nested before-after analysis. We used bivariate and multivariate analyses to compare obstetric management in the control vs intervention group. RESULTS: There were 25 977 deliveries in the three hospitals during the study period: 10 207 (39%) in the control and 15 770 (61%) in the intervention group. After adjusting for maternal age, ethnicity, education, gestational age, stage of labor at admission, complications during labor, and birthweight, the intervention group had a higher proportion of fetal heart rate monitoring performed as per protocol (adjusted odds ratio [aOR] 1.19, 95% confidence interval [CI] 1.12-1.27), shorter time intervals between each fetal heart rate monitoring (aOR 2.09, 95% CI 1.96-2.23), a higher likelihood of abnormal fetal heart rate being detected (aOR 1.53, 95% CI 1.25-1.68), progress of labor more often being recorded immediately after per vaginal examination (aOR 2.73, 95% CI 2.55-2.93), and partograph filled as per standards (aOR 3.18, 95% CI 2.98-3.50). The cesarean birth rate was 2.5% in the control group and 8.2% in the intervention group (aOR 3.12, 95% CI 2.64-3.68). CONCLUSIONS: The NePeriQIP intervention has potential to improve obstetric care, especially intrapartum fetal surveillance, in similar low-resource settings.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".