Scaling up newborn care technologies from tertiary- to secondary-level hospitals in Malawi: an implementation case study of health professional perspectives on bubble CPAP
Bibliographic record
Abstract
BACKGROUND: While Malawi has achieved success in reducing overall under-five mortality, reduction of neonatal mortality remains a persistent challenge. There has, therefore, been a push to strengthen the capacity for quality newborn care at district hospitals through the implementation of innovative neonatal technologies such as bubble continuous positive airway pressure (CPAP). This study investigates tertiary- versus secondary-level hospital differences in capacities for bubble CPAP use and implications for implementation policies. METHODS: A secondary analysis of interviews was conducted with 46 health workers at one tertiary hospital and three secondary hospitals in rural Southern Malawi. Grounded theory was utilized to explore the emerging themes according to health worker cadres (nurse, clinician, district health management) and facility level (tertiary- and secondary-level facilities), which were managed using NVivo 12 (QSR International, Melbourne, Australia). RESULTS: We identified frequent CPAP use and the availability of neonatal nurses, physicians, and reliable electricity as facilitators for CPAP use at the tertiary hospital. Barriers at the tertiary hospital included initiation eligibility disagreements between clinicians and nurses and insufficient availability of the CPAP machines. At secondary-level hospitals, the use was supported by decision-making and initiation by nurses, involving caretakers to assist in monitoring and reliable availability of CPAP machines. Bubble CPAP was hindered by unreliable electricity, staffing shortages and rotation policies, and poor systems of accountability. CONCLUSION: While this study looked at the implementation of bubble CPAP in Malawi, the findings may be applicable for scaling up other novel neonatal technologies in low-resource settings. Implementation policies must consider staffing and management structures at different health services levels for effective scale-up.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".