Assessment of Facility Readiness for Pediatric Emergency and Critical Care Utilizing a 2-Phase Survey Conducted in Six Hospitals in Uganda and Cameroon: A Quality Improvement Study
Bibliographic record
Abstract
OBJECTIVES: Each year, 5.3 million children under 5 years of age die in low-resource settings, often due to delayed recognition of disease severity, inadequate treatment, or a lack of supplies. We describe the use of a comprehensive digital facility-readiness survey tool, recently developed by the Pediatric Sepsis Data CoLaboratory, which aims to identify target areas for quality improvement related to pediatric emergency and critical care. METHODS: Facility-readiness surveys were conducted at six sub-Saharan African hospitals providing pediatric emergency and critical care in Uganda (n = 4) and Cameroon (n = 2). The tool is a 2-phase survey to assess readiness to provide pediatric essential emergency and critical care: (1) an "environmental scan," focusing on infrastructure, availability, and functionality of resources, and (2) an "observational scan" assessing the quality and safety of care through direct observation of patients receiving treatment for common diseases. Data were captured in a mobile application and the findings analyzed descriptively. RESULTS: Varying levels of facility readiness to provide pediatric emergency care were observed. Only 1 of 6 facilities had a qualified staff member to assess children for danger signs upon arrival, and only 2 of 6 had staff with skills to manage emergency conditions. Only 21% of essential medicines required for pediatric emergency and critical care were available at all six facilities. Most facilities had clean running water and soap or disinfectants, but most also experienced interruptions to their electricity supply. Less than half of patients received an appropriate discharge note and fewer received counseling on postdischarge care; follow-up was arranged in less than a quarter of cases. CONCLUSIONS: These pilot findings indicate that facilities are partially equipped and ready to provide pediatric emergency and critical care. This facility-readiness tool can be utilized in low-resource settings to assist hospital administrators and policymakers to determine priority areas to improve quality of care for the critically ill child.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".