Sharing psychosocial risk screening information with pediatric oncology healthcare providers: Service utilization and related factors
Bibliographic record
Abstract
BACKGROUND: Psychosocial morbidity in pediatric oncology patients and their caregivers is widely recognized. Although routine systematic psychosocial screening has been proposed as a standard of care, screening is still limited. The present study assessed whether supplying the patient's treating team of healthcare providers with psychosocial risk screening information near diagnosis would increase the rate of documented psychosocial contacts, particularly for patients/families with elevated risk. The effect of demographic and clinical factors was also examined. PROCEDURES: Ninety-three families with a child/youth newly diagnosed with cancer participated. Families were randomly assigned to a care as usual control group (n = 44) or an intervention group (n = 49) where the treating team was provided with a summary of family psychosocial risk, measured by the Psychosocial Assessment Tool (PAT). The PAT was completed by the primary caregiver, who also provided demographic information. The number of psychosocial intervention contacts documented in the medical charts was examined. RESULTS: The rate of psychosocial intervention did not significantly differ between the groups (P > 0.05). The intensity of the child's cancer treatment was found to be the only significant predictor of the number of documented psychosocial intervention contacts (β = 0.396, P < 0.001). CONCLUSIONS: Clinical factors appear to be more predictive of the rate of psychosocial intervention provided to pediatric oncology patients and their families than informing the treating team of family psychosocial risk. Additional research is required to address the gap between psychosocial risk screening, psychosocial intervention, and family outcomes.
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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 | 0.011 |
| 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) | 0.003 | 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".