Pediatric Emergency and Critical Care Resources and Infrastructure in Resource-Limited Settings: A Multicountry Survey*
Bibliographic record
Abstract
OBJECTIVES: To describe the infrastructure and resources for pediatric emergency and critical care delivery in resource-limited settings worldwide. DESIGN: Cross-sectional survey with survey items developed through literature review and revised following piloting. SETTING: The electronic survey was disseminated internationally in November 2019 via e-mail directories of pediatric intensive care societies and networks and using social media. PATIENTS: Healthcare providers who self-identified as working in resource-limited settings. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Results were summarized using descriptive statistics and resource availability was compared across World Bank country income groups. We received 328 responses (238 hospitals, 60 countries), predominantly in Latin America and Sub-Saharan Africa (n = 161, 67.4%). Hospitals were in low-income (28, 11.7%), middle-income (166, 69.5%), and high-income (44, 18.4%) countries. Across 174 PICU and adult ICU admitting children, there were statistically significant differences in the proportion of hospitals reporting consistent resource availability ("often" or "always") between country income groups (p < 0·05). Resources with limited availability in lower income countries included advanced ventilatory support, invasive and noninvasive monitoring, central venous access, renal replacement therapy, advanced imaging, microbiology, biochemistry, blood products, antibiotics, parenteral nutrition, and analgesic/sedative drugs. Seventy-seven ICUs (52.7%) were staffed 24/7 by a pediatric intensivist or anesthetist. The nurse-to-patient ratio was less than 1:2 in 71 ICUs (49.7%). CONCLUSIONS: Contemporary data demonstrate significant disparity in the availability of essential and advanced human and material resources for the care of critically ill children in resource-limited settings. Minimum standards for essential pediatric emergency and critical care in resource-limited settings are needed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".