The RICHER Social Pediatrics Model: Fostering Access and Reducing Inequities in Children's Health
Bibliographic record
Abstract
Considerable evidence shows that children and families who are vulnerable because of their social and material circumstances shoulder a disproportionate burden of disease and are more likely to face both social and structural challenges in accessing healthcare. Addressing these issues in children is particularly important as evidence has demonstrated that inequities in health are cumulative over the life course. In this article, the authors report on the RICHER (Responsive, Intersectoral-Interdisciplinary, Child-Community, Health, Education and Research) social pediatrics initiative, which was designed to foster timely access to healthcare across the spectrum from primary care to specialized services for a community of inner-city children who have disproportionately high rates of developmental vulnerability. Their research shows that the initiative has effectively "reformed" health services delivery to provide care in ways that are accessible and responsive to the needs of the population. RICHER is an intersectoral, interdisciplinary outreach initiative that delivers care through the formation of innovative partnerships. The authors share research results that demonstrate that the RICHER model of engagement with children and families not only effectively fosters access for families with multiple forms of disadvantage, but also improves outcomes by empowering parents of particularly vulnerable children to become more active participants in care.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".