Framework for systems of pediatric well-care visits: a scoping review
Bibliographic record
Abstract
BACKGROUND: Despite decades of global efforts to reduce under five mortality, 5 million children die before their fifth birthday. Numerous landmark reports have called for integrated approaches to further accelerate reductions, yet interventions are often delivered in silos and, to our knowledge, no global synthesis of the evidence for integrated preventive systems exists. We conducted a scoping review to map the existing literature on systems of well-care visits for children under five years from 2000 to 2023 globally. METHOD: A systematic search of relevant databases (MEDLINE, the Cochrane Library, Web of Science, and OVID Global Health) was conducted. In total, 8,945 unique documents were identified and after screening titles and abstracts, a total of 1587 articles were assessed for eligibility through full-text review. The information was extracted based on the WHO's established health system framework, and from these pillars, a conceptual framework was derived on the components and the potential impact on child health care. RESULTS: We found 322 eligible articles (217 primary articles, 105 reviews). Among the primary articles, close to 50% focused on high-income countries, while less than a fifth focused on low-income countries. Across regions, systems of preventive well-care visits are organized differently; from health days to home-visits, programs, and clinics. Depending on the context, the content differs (e.g., vaccinations, screenings, parent education) and the contextual challenges and solutions for implementation (e.g., mHealth, scale up existing structures). CONCLUSIONS: This review identifies core components of well-care visit systems, including interventions, workforce composition, and governance structures, alongside key implementation strategies such as the scaling up of existing service delivery models. We introduce a framework for systems of well-care visits. These systems may offer child health care improvements, including enhanced attendance rates, increased service utilization, and decreased health care expenditure, thereby contributing to improved child health care for all.
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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 | 0.071 | 0.084 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.053 | 0.046 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".