It takes a village: a realist synthesis of social pediatrics program
Bibliographic record
Abstract
OBJECTIVES: To better understand how social pediatric initiatives (SPIs) enact equitable, integrated, embedded approaches with high-needs children and families while facilitating proportionate distribution of health resources. METHODS: The realist review method incorporated the following steps: (1) identifying the review question, (2) formulating the initial theory, (3) searching for primary studies, (4) selecting and appraising study quality, (5) synthesizing relevant data and (6) refining the theory. RESULTS: Our analysis identified four consistent patterns of care that may be effective in social pediatrics: (1) horizontal partnerships based on willingness to share status and power; (2) bridged trust initiated through previously established third party relationships; (3) knowledge support increasing providers' confidence and skills for engaging community; and (4) increasing vulnerable families' self-reliance through empowerment strategies. CONCLUSIONS: This research is unique because it focused on "how" outcomes are achieved and offers insight into the knowledge, skills and philosophical orientation clinicians need to effectively deliver care in SPIs. Research insights offer guidance for organizational leaders with a mandate to address child and youth health inequities and may be applicable to other health initiatives.
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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 | 0.030 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".