Perceived benefits of and barriers to psychosocial risk screening in pediatric oncology by health care providers
Bibliographic record
Abstract
BACKGROUND: Although systematic psychosocial screening has been established as a standard of psychosocial care in pediatric oncology, this is not yet widely implemented in clinical practice. Limited information is available regarding the reasons behind this. In this study, we investigated perceptions of psychosocial screening by health care providers (HCPs) involved in pediatric cancer care. METHODS: Using purposeful sampling, 26 HCPs (11 oncologists, 8 nurses, and 7 social workers) from a large North American pediatric cancer center participated in semistructured interviews. Interviews were recorded and transcribed verbatim. Themes were then derived using content analysis. RESULTS: The themes were organized into perceived benefits of and barriers to psychosocial risk screening, and practical issues regarding implementation. Perceived benefits of screening included obtaining concise documentation of family psychosocial risk, identifying psychosocial factors important to medical treatment, starting a conversation, and triaging patients to psychosocial services. Barriers included perceived limited institutional support, commitment, and resources for psychosocial services, limited knowledge and appreciation of existing evidence-based validated tools, concerns about diverse family cultural backgrounds regarding psychosocial issues and language proficiency, and HCPs' personal values regarding psychosocial screening. Finally, practical issues of implementation including training in psychosocial risk screening, when and how to screen were discussed. CONCLUSIONS: These findings highlight the importance of addressing HCPs' perceptions of benefits, barriers, and practical issues regarding implementing psychosocial risk screening.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".