Remote Mentorship to Improve Continuous Positive Airway Pressure Use in the Ethiopian Neonatal Network
Bibliographic record
Abstract
BACKGROUND: Continuous positive airway pressure (CPAP) is an evidence-based therapy for respiratory distress syndrome (RDS), the leading cause of death for preterm infants globally. Ethiopian Neonatal Network (ENN) teams identified a quality gap in CPAP use for preterm infants with RDS. We sought to use remote education and mentorship without additional resources to improve CPAP use in this population. METHODS: Nineteen public ENN hospitals participated in this quality improvement (QI) collaborative (September 2021 to September 2022). The primary intervention was implementation of a telementoring program. Five Ethiopian nurse-physician mentor pairs each supported 4 mentee hospitals through a remote CPAP optimization training package. Quarterly ENN collaborative meetings reviewed progress and challenges. Hospitals submitted patient-level data for all neonatal unit admissions and monthly audits. Run chart rules were used to assess for nonrandom evidence of change related to CPAP use. Preterm mortality in preintervention and postintervention periods was evaluated by a χ2 test. RESULTS: The launch of the QI collaborative with remote education and mentoring coincided with an increase in the documentation of Downes score on admission (57.8% to 95.6%) and number of preterm infants (<37 weeks' gestation) receiving CPAP (129 to 138 per month). Preterm mortality decreased significantly from preintervention (28%) to postintervention periods (21.6%) (P < .0001). CONCLUSION: Ethiopian nurse-physician mentorship pairs supporting mentee hospitals in standardizing assessments of respiratory distress and optimizing the use of CPAP increased the number of preterm infants treated with CPAP. Significant reduction in preterm mortality postintervention is encouraging as CPAP use continues to scale up globally.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".