Provision of Surgical Care for Children Across Somaliland: Challenges and Policy Guidance
Bibliographic record
Abstract
BACKGROUND: Existing data suggest a large burden of surgical conditions in low- and middle-income countries (LMICs). However, surgical care for children in LMICs remains poorly understood. Our goal was to define the hospital infrastructure, workforce, and delivery of surgical care for children across Somaliland and provide policy guidance to improve care. METHODS: We used two established hospital assessment tools to assess infrastructure, workforce, and capacity at all hospitals providing surgical care for children across Somaliland. We collected data on all surgical procedures performed in children in Somaliland between August 2016 and July 2017 using operative logbooks. RESULTS: Data were collected from 15 hospitals, including eight government, five for-profit, and two not-for-profit hospitals. Children represented 15.9% of all admitted patients, and pediatric surgical interventions comprised 8.8% of total operations. There were 0.6 surgical providers and 1.2 anesthesia providers per 100,000 population. A total of 1255 surgical procedures were performed in children in all hospitals in Somaliland over 1 year, at a rate of 62.4 surgical procedures annually per 100,000 children. Care was concentrated at private hospitals within urban areas, with a limited number of procedures for many high-burden pediatric surgical conditions. CONCLUSIONS: We found a profound lack of surgical capacity for children in Somaliland. Hospital-level surgical infrastructure, workforce, and care delivery reflects a severely resource-constrained health system. Targeted policy to improved essential surgical care at local, regional, and national levels is essential to improve the health of children in Somaliland.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".