The Injury Prevention Program to Reduce Early Childhood Injuries: A Cluster Randomized Trial
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The American Academy of Pediatrics designed The Injury Prevention Program (TIPP) in 1983 to help pediatricians prevent unintentional injuries, but TIPP's effectiveness has never been formally evaluated. We sought to evaluate the impact of TIPP on reported injuries in the first 2 years of life. METHODS: We conducted a stratified, cluster-randomized trial at 4 academic medical centers: 2 centers trained their pediatric residents and implemented TIPP screening and counseling materials at all well-child checks (WCCs) for ages 2 to 24 months, and 2 centers implemented obesity prevention. At each WCC, parents reported the number of child injuries since the previous WCC. Proportional odds logistic regression analyses with generalized estimating equation examined the extent to which the number of injuries reported were reduced at TIPP intervention sites compared with control sites, adjusting for baseline child, parent, and household factors. RESULTS: A total of 781 parent-infant dyads (349 TIPP; 432 control) were enrolled and had sufficient data to qualify for analyses: 51% Hispanic, 28% non-Hispanic Black, and 87% insured by Medicaid. Those at TIPP sites had significant reduction in the adjusted odds of reported injuries compared with non-TIPP sites throughout the follow-up (P = .005), with adjusted odds ratios (95% CI) of 0.77 (0.66-0.91), 0.60 (0.44-0.82), 0.32 (0.16-0.62), 0.26 (0.12-0.53), and 0.27 (0.14-0.52) at 4, 6, 12, 18, and 24 months, respectively. CONCLUSIONS: In this cluster-randomized trial with predominantly low-income, Hispanic, and non-Hispanic Black families, TIPP resulted in a significant reduction in parent-reported injuries. Our study provides evidence for implementing the American Academy of Pediatrics' TIPP in routine well-child care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".