Healthcare Professionals’ Knowledge of Family Psychosocial Problems in Pediatric Cancer
Bibliographic record
Abstract
BACKGROUND: Best practice guidelines for the treatment of cancer now advocate for a child- and family-centered model of care and a psychosocial model of risk prevention. However, healthcare professionals (HCPs) report a number of barriers preventing the implementation of psychosocial care, including an absence of tools to help identify psychosocial problems within the family. OBJECTIVES: The aims of this study are to (1) explore the psychometric properties of the Psychosocial Care Checklist (PCCL) and (2) test if the PCCL can differentiate the degree to which HCPs are aware of psychosocial problems within the family (patient, siblings, parents) of a child with cancer. METHODS: Thirty-seven HCPs caring for a child with cancer completed the PCCL at time 1 (2-4 weeks after diagnosis) and 29 HCPs completed the PCCL at time 2 (2-3 weeks after). RESULTS: The PCCL had strong test-retest reliability for all domains (α > .60) and strong internal consistency for the total PCCL (α = .91). Interrater reliability was moderate for the oncologist-nurse dyad with regard to sibling knowledge (r = 0.56) and total psychosocial knowledge (r = 0.65). Social workers were significantly more knowledgeable than both nurses and oncologists about total family problems (P = .01) and sibling problems (P = .03). CONCLUSIONS: Preliminary findings suggest that the PCCL has adequate test-retest reliability and validity and is useful in differentiating the degree to which HCPs are aware of psychosocial problems within the family, with social workers being the most knowledgeable. IMPLICATIONS FOR PRACTICE: Using the PCCL may help HCPs to identify psychosocial problems within the family and appropriately allocate psychosocial resources.
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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 | 0.017 |
| 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 | 0.001 |
| 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".