Pediatric emergency care capacity in a low-resource setting: An assessment of district hospitals in Rwanda
Bibliographic record
Abstract
BACKGROUND: Health system strengthening is crucial to improving infant and child health outcomes in low-resource countries. While the knowledge related to improving newborn and child survival has advanced remarkably over the past few decades, many healthcare systems in such settings remain unable to effectively deliver pediatric advance life support management. With the introduction of the Emergency Triage, Assessment and Treatment plus Admission care (ETAT+)-a locally adapted pediatric advanced life support management program-in Rwandan district hospitals, we undertook this study to assess the extent to which these hospitals are prepared to provide this pediatric advanced life support management. The results of the study will shed light on the resources and support that are currently available to implement ETAT+, which aims to improve care for severely ill infants and children. METHODS: A cross-sectional survey was undertaken in eight district hospitals across Rwanda focusing on the availability of physical and human resources, as well as hospital services organizations to provide emergency triage, assessment and treatment plus admission care for severely ill infants and children. RESULTS: Many of essential resources deemed necessary for the provision of emergency care for severely ill infants and children were readily available (e.g. drugs and laboratory services). However, only 4/8 hospitals had BVM for newborns; while nebulizer and MDI were not available in 2/8 hospitals. Only 3/8 hospitals had F-75 and ReSoMal. Moreover, there was no adequate triage system across any of the hospitals evaluated. Further, guidelines for neonatal resuscitation and management of malaria were available in 5/8 and in 7/8 hospitals, respectively; while those for child resuscitation and management of sepsis, pneumonia, dehydration and severe malnutrition were available in less than half of the hospitals evaluated. CONCLUSIONS: Our assessment provides evidence to inform new strategies to enhance the capacity of Rwandan district hospitals to provide pediatric advanced life support management. Identifying key gaps in the health care system is required in order to facilitate the implementation and scale up of ETAT+ in Rwanda. These findings also highlight a need to establish an outreach/mentoring program, embedded within the ongoing ETAT+ program, to promote cross-hospital learning exchanges.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".