Healthy Steps: A Systematic Review of a Preventive Practice-Based Model of Pediatric Care
Bibliographic record
Abstract
The preventive role of anticipatory guidance in pediatric practice has gained increasing importance over the last two decades, resulting in the development of competing models of practice-based care. Our goal was to systematically evaluate and summarize the literature pertaining to the Healthy Steps Program for Young Children, a widely cited and utilized preventive model of care and anticipatory guidance, Medline and the bibliographies of review articles for relevant studies were searched using the keywords: Healthy Steps, preventive care, pediatric practice and others. Other sources included references of retrieved publications, review articles, and books; government documents; and Internet sources. Relevant sources were selected on the basis of their empirical evaluation of some component of care (e.g., child outcomes, parent outcomes, quality of care). From 21 identified articles, 13 met the inclusion criteria of empirical evaluation. These evaluations were summarized and compared. Results indicated that the Healthy Steps program has been rigorously evaluated and shown to be effective in preventing negative child and parent outcomes and enhancing positive outcomes. Despite limited information concerning cost effectiveness, the Healthy Steps Program provides clear benefit through early screening, family-centered care, and evidence-based anticipatory guidance. It is recommended that the Healthy Steps program be more widely disseminated to relevant stakeholders, and further enhanced by improved linguistic and cultural sensitivity and long term evaluation of cost effectiveness.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".