Advocacy for Children With Surgical Diseases in Nigeria: National Policy Status, Gaps, and Solutions
Bibliographic record
Abstract
INTRODUCTION: An estimated 1.7 billion children, mostly in low- and middle-income countries, lack access to surgical care. Increased focus on, and investment in, children's surgery requires the deliberate and strategic inclusion of children's surgery in healthcare policies. Here we evaluate the status of children's surgical diseases in Nigeria's healthcare policies. METHODS: Key Nigerian policy documents referring to child and adolescent health were identified and analyzed using Collins' 8-step framework for health policy analysis. The search for evidence (3rd step in Colin's framework) included a combination of directed (DCA) and conventional content analysis (CCA). DCA was based on 4 categories (workforce, service delivery, infrastructure and financing) obtained from the surgical systems development framework developed by the Lancet Commission on Global Surgery. RESULTS: Seven policy documents with child and adolescent health contents were reviewed: the National Child Health Policy (NCHP), National Policy on Development of Adolescents and Young People in Nigeria (NPDAYPN), Nigeria Every Newborn Action Plan (NENAP), Community Health Influencers Promoters and Services Programme (CHIPS), National Surgical Obstetrics Anaesthesia and Nursing Plan (NSOANP), National Guidelines for Comprehensive Newborn Care (NGCNC) and National Strategic Health Development Plan (NSHDP). Only the NSOANP had surgeons involved in its development, comprehensively addressed children's surgical conditions across all categories, and included surgical stakeholders in its implementation. CONCLUSION: Children's surgery is not prioritized for specific inclusion within Nigeria's healthcare policies. There is a need for greater collaboration and integration into key healthcare policies. Prioritizing deliberate and strategic inclusion of children's surgery will ensure unmet surgical needs is addressed.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".