Prevalence of Posttraumatic Stress Disorder among Children and Adolescents following Road Traffic Accidents: A Meta-Analysis
Bibliographic record
Abstract
Objective: Children and adolescents are among the most vulnerable road users, and road traffic accidents (RTAs) can lead to not only physical injuries but also adverse psychological outcomes, particularly posttraumatic stress disorder (PTSD). However, estimates of the prevalence of PTSD among children and adolescents following RTAs varied considerably across studies. Therefore, this study aimed to estimate the pooled prevalence of PTSD among this population. Methods: A systematic search for literature was performed in the electronic databases of PubMed, Web of Science, PsycINFO, and Embase. Heterogeneity was assessed using the Cochran’s chi-square test and quantified by the I 2 value. Meta-regression analyses were carried out to identify the effects of some potential moderators on the overall heterogeneity. Subgroup analyses were performed to estimate the pooled prevalence of PTSD according to some sample characteristics. Results: Eleven eligible studies with a total of 1532 children and adolescents who were involved in RTAs were included. The overall heterogeneity ( I 2 = 89.7, P < 0.001) was high across the eligible studies, and the pooled prevalence of PTSD was 19.95% (95% confidence interval, 13.63% to 27.09%) by a random-effects model. No significant moderators of the overall heterogeneity were identified using meta-regression analyses. Subgroup analyses showed that the pooled prevalence of PTSD differed significantly according to the study location and gender ( P < 0.05). Conclusions: One-fifth of children and adolescents developed PTSD in the aftermath of RTAs, indicating the need for regular assessment of PTSD and timely and effective psychological interventions among this population. Furthermore, more population-based studies with a large sample size are warranted. The protocol was registered in the PROSPERO database (No. CRD42018087941).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".