Scaling Up Safer Birth Bundle Through Quality Improvement in Nepal (SUSTAIN)—a stepped wedge cluster randomized controlled trial in public hospitals
Bibliographic record
Abstract
BACKGROUND: Each year, 2.2 million intrapartum-related deaths (intrapartum stillbirths and first day neonatal deaths) occur worldwide with 99% of them taking place in low- and middle-income countries. Despite the accelerated increase in the proportion of deliveries taking place in health facilities in these settings, the stillborn and neonatal mortality rates have not reduced proportionately. Poor quality of care in health facilities is attributed to two-thirds of these deaths. Improving quality of care during the intrapartum period needs investments in evidence-based interventions. We aim to evaluate the quality improvement package-Scaling Up Safer Bundle Through Quality Improvement in Nepal (SUSTAIN)-on intrapartum care and intrapartum-related mortality in public hospitals of Nepal. METHODS: We will conduct a stepped wedge cluster randomized controlled trial in eight public hospitals with each having least 3000 deliveries a year. Each hospital will represent a cluster with an intervention transition period of 2 months in each. With a level of significance of 95%, the statistical power of 90% and an intra-cluster correlation of 0.00015, a study period of 19 months should detect at least a 15% change in intrapartum-related mortality. Quality improvement training, mentoring, systematic feedback, and a continuous improvement cycle will be instituted based on bottleneck analyses in each hospital. All concerned health workers will be trained on standard basic neonatal resuscitation and essential newborn care. Portable fetal heart monitors (Moyo®) and neonatal heart rate monitors (Neobeat®) will be introduced in the hospitals to identify fetal distress during labor and to improve neonatal resuscitation. Independent research teams will collect data in each hospital on intervention inputs, processes, and outcomes by reviewing records and carrying out observations and interviews. The dose-response effect will be evaluated through process evaluations. DISCUSSION: With the global momentum to improve quality of intrapartum care, better understanding of QI package within a health facility context is important. The proposed package is based on experiences from a similar previous scale-up trial carried out in Nepal. The proposed evaluation will provide evidence on QI package and technology for implementation and scale up in similar settings. TRIAL REGISTRATION NUMBER: ISRCTN16741720 . Registered on 2 March 2019.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".