Psychosocial Risk, Symptom Burden, and Concerns in Families Affected by Childhood Cancer
Bibliographic record
Abstract
Abstract PURPOSE: The revised Psychosocial Assessment Tool (PATrev) is a common family-level risk-based screening tool for pediatric oncology that has gained support for its ability to predict, at diagnosis, the degree of psychosocial support a family may require throughout the treatment trajectory. However, ongoing screening for symptoms and concerns (e.g., feeling alone, understanding treatment) remain underutilized. Resource limitations necessitate triaging and intervention based on need and risk. Given the widespread use of the PATrev, we sought to explore the association between family psychosocial risk, symptom burden (as measured by the revised Edmonton Symptom Assessment System; ESAS-r), and concerns (as measured by the Canadian Problem Checklist; CPC). METHODS: Families (n = 85) with children between 2–18 years of age (M = 11.98, male: 62.4%) on or off treatment for cancer were recruited from the Alberta Children’s Hospital. One parent from each family completed the PATrev and the CPC. Participants 8–18 years of age completed the ESAS-r. RESULTS: Risk category (unviersal/low risk = 67.1%, targeted/intermediate risk = 21.1%, clinical/high risk = 5.9%), predicted symptom burden (F[2, 63.07] = 4.57, p = .014) and concerns (F[2, 80.08] = 16.34, p < .001), such that universal risk was associated with significantly lower symptom burden and fewer concerns. CONCLUSION: Family psychosocial risk is associated with cross-sectionally identified concerns and symptom burden, suggesting that resources might be prioritized for families with the greatest predicted need. Future research should evaluate the predictive validity of the PATrev to identify longitudinal concerns and symptom burden throughout the cancer trajectory.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".