Preventing Unintentional Injuries in School-Aged Children: A Systematic Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Unintentional injuries constitute the leading causes of death and long-term disabilities among children aged 5 to 15 years. We aimed to systematically review published literature on interventions designed to prevent unintentional injuries among school-aged children. METHODS: We searched MEDLINE, PubMed, Embase, Cochrane Central Register of Controlled Trials, CINAHL, and PsycINFO and screened the reference lists of included studies and relevant reviews. We included randomized controlled trials, controlled before-and-after studies, and interrupted time series studies. The focus of included studies was on primary prevention measures. Two reviewers collected data on type of study design, setting, population, intervention, types of injuries, outcomes assessed, and statistical results. RESULTS: Of 30 179 identified studies, 117 were included in this review. Most of these studies were conducted in high-income countries and addressed traffic-related injuries. Evidence from included studies reveals that multicomponent educational interventions may be effective in improving safety knowledge, attitudes, and behaviors in school-aged children mainly when coupled with other approaches. Laws/legislation were shown to be effective in increasing cycle helmet use and reducing traffic-related injury rates. Findings reveal the relevance of infrastructure modification in reducing falls and improving pedestrian safety among children. CONCLUSIONS: Additional studies are needed to evaluate the impact of unintentional injury prevention interventions on injury, hospitalizations, and mortality rates and the impact of laws and legislation and infrastructure modification on preventing unintentional injuries among school-aged children.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".