Integrating dermatology services into a social pediatrics network: 8 years of experience in the RICHER (Responsive, Interdisciplinary/Intersectoral, Child/Community, Health, Education and Research) program
Bibliographic record
Abstract
Social pediatric initiatives aim to improve health outcomes for vulnerable children by working in the community to empower families, to enhance protective factors that mitigate adverse childhood experiences (ACEs), and to deliver place-based health care. In 2012, pediatric dermatology was added as a component of the Responsive, Interdisciplinary Intersectoral Child and Community Health Education and Research (RICHER) social pediatric program in Vancouver, BC. We share our experience with inclusion of pediatric dermatology in a well-established social pediatric program as well as lessons we have learned in the first 8 years of our partnership. Partnership, bridging trust, knowledge sharing, empowerment, consistency, and flexibility were found to be central elements in the success of this endeavor.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".