Does the use of the revised Psychosocial Assessment Tool (PATrev) result in improved quality of life and reduced psychosocial risk in Canadian families with a child newly diagnosed with cancer?
Bibliographic record
Abstract
OBJECTIVES: Early psychosocial screening may guide interventions and ameliorate the adverse psychosocial effects of childhood cancer. The revised psychosocial assessment tool provides risk information - Universal (typical distress), Targeted (additional specific distress), and Clinical (severe distress) - about the child with cancer and his or her family. This pilot study investigated the benefits of providing a summary of family psychosocial risk information to the medical team treating the newly diagnosed child (Experimental Group, EG). METHOD: We conducted a pilot randomized control trial with a sample of 67 parents, comparing the EG to the control group (CG) on parental perception of family psychosocial difficulties (revised psychosocial assessment tool risk levels), child behavior (behavior assessment scale for children-2), pediatric quality of life (PedsQL), and parental anxiety (state-anxiety scale of the state-trait anxiety inventory ), 2-4 weeks after diagnosis (Time 1) and 6 months later (Time 2). RESULTS: Compared to the CG, participants in the EG had significantly reduced targeted and clinical risk (p < 0.001), and improved pain related PedsQL at Time 2 (p < 0.05). Scores for PedsQL total and nearly all subscales improved over time in both groups (p < 0.05 to p < 0.001). No changes in behavior scores were noted. CONCLUSION: Preliminary findings suggest that providing a summary of the Psychosocial Assessment Tool to the treating team shortly after diagnosis may help reduce family wide psychosocial risk 6 months later and improve quality of life related to pain for children who are undergoing treatment for cancer.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".