Siblings' Experiences with Psychosocial Support Throughout the Pediatric Cancer Trajectory
Bibliographic record
Abstract
Siblings of children with cancer are exposed to significant stress and experience challenges in many aspects of their lives throughout the pediatric cancer trajectory. As a result, siblings are at risk for acute and long-term psychosocial consequences and must be provided with appropriate supportive services during and after cancer treatment. At this time, there are no evidence-based standards to guide the supportive care of siblings of children with cancer. The purpose of this study was to explore and develop an understanding of the different ways in which siblings of children with cancer perceive their psychosocial needs are addressed from formal and informal sources. A qualitative interpretive descriptive methodology was used in the study design. Data were collected from a sample of ten siblings in Ontario whose siblings were diagnosed and treated for pediatric cancer within the past ten years. The data collection methods included semi-structured individual interviews, socio-demographic questionnaires, field notes, and the researcher’s reflexivity journal. A reflexive thematic and inductive approach was used for the analysis of the collected data. The study findings highlight that there are significant gaps in the psychosocial support provided to siblings of children with cancer. Four overarching issues were identified, including: (1) siblings’ isolation from the pediatric cancer experience, (2) limited parental awareness of siblings’ psychosocial needs, (3) needs for consistent academic and school support, and (4) needs for standardized psychosocial support from healthcare professionals and hospital systems. The study findings provide several implications for clinical practice, policy, education, and research for nurses and other healthcare professionals working in pediatric oncology settings. Recommendations include: (1) integration of psychosocial care for siblings into clinical practice, (2) development of evidence-based standards and guidelines for the psychosocial support of siblings, (3) ongoing education and training for healthcare professionals related to siblings’ psychosocial healthcare needs, and (4) additional research on standardized screening tools and interventions for siblings.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".