Exploring the Prevalence of Post-traumatic Stress Disorder and Post-traumatic Stress Symptoms in Parents Within 12 Months of Child Burn Injury: A Systematic Review
Bibliographic record
Abstract
Our systematic review aimed to investigate the prevalence of post-traumatic stress symptoms (PTSS) and post-traumatic stress disorder (PTSD) among parents within 12 months of their child's burn injury. A literature search was conducted in PubMed, Embase, Web of Science, Psychinfo, and CINAHL on January 6, 2023, for quantitative studies reporting the prevalence of PTSD and/or PTSS in parents within 12 months following their child's burn injury. The risk of bias was assessed using the Mixed Methods Appraisal Tool version 2018. A narrative synthesis of prevalence was presented. We identified 15 articles that met our inclusion criteria. The prevalence of PTSS within 12 months following the burn injury ranged from 6% to 49%. Prevalence estimates of PTSD within the 12 months following a burn injury were limited, ranging from 4.4% to 22%. Our findings highlight the significant impact of burn injuries on parental mental health, with a considerable proportion of parents experiencing PTSS within 12 months following their child's burn injury. Prevalence estimates for PTSD were limited and warrant further investigation. Our review also underscores the need for standardization of PTSS/PTSD terminology. Timely and targeted psychological support is needed for parents in the aftermath of their child's burn injury.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".