Psychosocial screening and mental health in pediatric cancer: A randomized controlled trial.
Bibliographic record
Abstract
OBJECTIVE: Diagnosis and treatment of childhood cancer can impact the mental health of the family. Early psychosocial risk screening may help guide interventions. The primary aim of this study was to evaluate if an intervention (providing psychosocial risk information to the patient's treating team) would result in decreased depression symptoms in caregivers, in general, and relative to initial psychosocial risk. A secondary aim was to examine intervention effects in a small sample of patient and sibling self-reported outcomes. METHODS: We randomly allocated families to the intervention group (IG, treating team received PAT summary) or control group (CG, no summary). One hundred and twenty-two caregivers of children newly diagnosed with cancer completed measures of depression and anxiety and psychosocial risk 2-4 weeks from diagnosis (T1) and 6 months later (T2). Patients and siblings completed self-report measures of depression and anxiety. RESULTS: = .60). Similar results were found in anxiety scores. Intervention effects with patients and siblings were inconclusive. CONCLUSIONS: Sharing psychosocial risk information with the treating team had measurable impact on mental health outcomes only if caregivers had initial high psychosocial risk. This study contributes to our understanding of mapping psychosocial screening and resources to improve outcomes in families managing childhood cancer. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".