Healthcare Providers’ Perceptions of the Utility of Psychosocial Screening Tools in Childhood Cancer: A Pilot Study
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To examine the perceptions of healthcare providers (HCPs) regarding the utility of two psychosocial screening tools designed for pediatric oncology, the Psychosocial Assessment Tool-Revised (PATrev) and the Psychosocial Care Checklist (PCCL). DESIGN: Repeated measures comparative study. SETTING: Four pediatric health centers in Ontario, Canada. SAMPLE: 15 oncologists, 14 nurses, and 8 social workers. METHODS: Using a visual analog scale (VAS), participants were asked to rate how useful they found (a) the psychosocial summary derived from the parent-completed PATrev, used to assess family psychosocial risk, and (b) the HCP-completed PCCL, used to identify family psychosocial needs. Measures were completed soon after diagnosis and six months later. Mann-Whitney U tests were used for analyses. MAIN RESEARCH VARIABLE: VAS scores. FINDINGS: Pediatric oncology HCPs differ in their acceptance of the psychosocial screening tools tested. The highest utility ratings for both instruments were from nurses, and the lowest utility ratings were from social workers; moderate ratings were obtained from oncologists. CONCLUSIONS: Psychosocial screening tools can identify the psychosocial needs of children with cancer and their families throughout the cancer trajectory. Consequently, these tools could foster communication among colleagues (medical and nonmedical) who are caring for children with cancer about the psychosocial needs of this population and the allocation of resources to address those needs. IMPLICATIONS FOR NURSING: Nurses seem to value these tools more than other HCPs, which may have positive implications for their clinical practice.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".