Tailoring a Child Injury Prevention Program for Low-Income U.S. Families
Bibliographic record
Abstract
Objective: Unintentional injuries are the leading cause of death for children in the United States, and young children ages 1 to 4 years are particularly at risk. Supervising for Home Safety (SHS) is a Canadian intervention that has been shown to reduce children’s injury risk by increasing caregiver supervision. Given that low-income children are at greatest risk for injury, this study describes a process of modifying the SHS program to be culturally appropriate for low-income families of U.S. preschool children. Method: Two rounds of focus groups were completed; feedback from the first round of focus groups was used to modify program materials prior to the second round. Results: Caregivers gleaned important take-away messages from both the original and modified materials, including the idea that injuries can happen quickly and caregivers can prevent injuries. Modifications to the intervention included increased diversity in the families represented in the videos as well as inclusion of U.S. injury statistics. Caregivers in both rounds of focus groups noted that the program messages were relatable and realistic and that the materials were impactful in increasing their awareness of children’s injury risk. Conclusion: We were able to successfully modify the SHS program to be appropriate for low-income U.S. families while preserving the core program messages. Implications for Impact Statement Focus groups with caregivers from U.S. preschool programs serving low-income children found that a child injury prevention program was successful in increasing caregivers’ awareness of the importance of supervising children more closely to prevent injuries. Based on caregiver feedback, changes to the program were made to make it more culturally relevant to low-income U.S. families.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".