Qualitative and Quantitative Evaluation of the Make Safe Happen App: Mobile Technology–Based Safety Behavior Change Intervention for Parents
Bibliographic record
Abstract
BACKGROUND: Nearly half of the unintentional injuries in children happen in and around the home; many of these injuries are preventable. Providing parents and caregivers with proper injury prevention information that is easily accessible may help them make their homes safer for children. OBJECTIVE: The aim of this study was to evaluate parental injury prevention awareness and home safety behaviors, motivations for and challenges to taking injury prevention and safety actions for parents as well as user experience following the use of the Make Safe Happen mobile app. METHODS: A total of 40 parents with children aged 0-12 years living in Columbus, Ohio, participated in 1 of 5 focus group discussions following the completion of (1) a pretest survey, (2) use of the Make Safe Happen app, and (3) a posttest survey. RESULTS: Following the use of the Make Safe Happen app, parents reported a significant increase in injury prevention awareness and completed 45% more home safety behaviors in and around their homes. Nearly all of the parents felt the app provided them with the information needed to make their home safer for their children; the great majority of parents intended to make such changes in the future. CONCLUSIONS: The combination of qualitative and quantitative data collection allowed for rich data capture and provided a deeper understanding of parents' safety knowledge, behaviors, app use, and decision making regarding child injury prevention in and around the home. The Make Safe Happen app provides the information and motivation parents and caregivers need to help them take steps to prevent child injuries that may occur in and around their homes.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".