Evidence of Interventions for the Prevention of Unintentional Injuries: Scoping Review
Bibliographic record
Abstract
Background: Unintentional injuries are the leading cause of death and disability among young children. Preventive strategies for unintentional injuries are mainly based on surveillance data and identifying risk factors. Objective: This study aimed to review and synthesize published literature that determined the effectiveness of interventions for preventing unintentional injuries among children. Methods: The methodological framework was supported by The Joanna Briggs Institute Reviewer's Manual - Methodology for JBI Scoping Reviews as well as the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The inclusion criteria to include the studies in the review were unintentional injuries in children, interventions to prevent injuries, a brief description of the intervention and the outcome of the intervention, and articles published in a peer-reviewed journal and written in the English language. Results: In total, 21 articles were included in the review following the systematic search of key databases such as Web of Science, PubMed/MEDLINE, Scopus, ScienceDirect, and gray literature for studies published between July 2013 and May 2023. Of the 21 articles, 16 were randomized controlled trials, 4 were nonrandomized controlled trials, and 1 was a mixed method study. The findings of the review showed that interventions, either as a single measure (video-based teaching, testimonial story-based teaching, health education, storybook reading) or in combination (knowledge quiz and simulation test, module-based teaching along with personal counseling, and teaching with the help of video and poster), have shown a considerable decline in the number and severity of injuries. The studies included various target populations, including children and adolescents between 0 and 19 years old. Conclusions: The review results indicate the need to plan, implement, and reinforce preventive measures and techniques to reduce unintentional injuries among children. They can also serve as a useful indicator for policymakers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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".