Assessing the inclusion of children’s surgical care in National Surgical, Obstetric and Anaesthesia Plans: a policy content analysis
Bibliographic record
Abstract
OBJECTIVE: While National Surgical, Obstetric and Anaesthesia Plans (NSOAPs) have emerged as a strategy to strengthen and scale up surgical healthcare systems in low/middle-income countries (LMICs), the degree to which children's surgery is addressed is not well-known. This study aims to assess the inclusion of children's surgical care among existing NSOAPs, identify practice examples and provide recommendations to guide inclusion of children's surgical care in future policies. DESIGN: We performed two qualitative content analyses to assess the inclusion of children's surgical care among NSOAPs. We applied a conventional (inductive) content analysis approach to identify themes and patterns, and developed a framework based on the Global Initiative for Children's Surgery's Optimal Resources for Children's Surgery document. We then used this framework to conduct a directed (deductive) content analysis of the NSOAPs of Ethiopia, Nigeria, Rwanda, Senegal, Tanzania and Zambia. RESULTS: Our framework for the inclusion of children's surgical care in NSOAPs included seven domains. We evaluated six NSOAPs with all addressing at least two of the domains. All six NSOAPs addressed 'human resources and training' and 'infrastructure', four addressed 'service delivery', three addressed 'governance and financing', two included 'research, evaluation and quality improvement', and one NSOAP addressed 'equipment and supplies' and 'advocacy and awareness'. CONCLUSIONS: Additional focus must be placed on the development of surgical healthcare systems for children in LMICs. This requires a focus on children's surgical care separate from adult surgical care in the scaling up of surgical healthcare systems, including children-focused needs assessments and the inclusion of children's surgery providers in the process. This study proposes a framework for evaluating NSOAPs, highlights practice examples and suggests recommendations for the development of future policies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".