Prevalence of anxiety, depression, and posttraumatic stress disorder in parents of children with cancer: A meta‐analysis
Bibliographic record
Abstract
BACKGROUND: For parents, a diagnosis of cancer in their child is a traumatic experience. However, there is conflicting evidence about the risk of developing mental illness among parents following diagnosis. Our objective was to conduct a meta-analysis to determine the prevalence of mental illness in parents of children with cancer. METHODS: Four databases were searched to identify articles describing the prevalence of anxiety, depression, or posttraumatic stress disorder (PTSD) in parents of pediatric cancer patients. Two reviewers independently screened and extracted data. Subgroup analyses by gender and phase of cancer experience were selected a priori. Studies were reviewed in accordance with PRISMA guidelines. RESULTS: Of 11 394 articles identified, 58 met inclusion criteria. Reported prevalence was highly heterogeneous, ranging from 5% to 65% for anxiety (pooled prevalence 21% [95% CI, 13%-35%]), 7% to 91% for depression (pooled prevalence 28% [95% CI, 23%-35%]), and 4% to 75% for PTSD (pooled prevalence 26% [95% CI, 22%-32%]). Prevalence was consistently higher than noncancer parental controls. Heterogeneity was not explained by parental gender or child's cancer phase and was instead likely due to significant methodological differences in measurement tools and defined thresholds. CONCLUSIONS: Parents of children with cancer have a higher prevalence of anxiety, depression, and PTSD compared with population controls. Yet, the reported prevalence of mental illness was highly variable, hampering any conclusive findings on absolute prevalence. To better understand the risk of long-term mental illness in this population and target interventions, future studies must adhere to standardized reporting and methods.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.056 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 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".