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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.071 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".