Siblings of Young People with Cancer: Medical Knowledge, Well-being and Adjustment
Bibliographic record
Abstract
Childhood and adolescent cancer is a significant health issue globally, with varying survival rates across countries. While advancements in cancer treatment have improved survival rates, the impact of cancer on the affected child's family, particularly siblings, remains poorly understood. Siblings often experience disruptions in family dynamics, attention disparities, and increased responsibilities due to their brother or sister's illness. Psychological consequences, such as anxiety and depression, have been reported in siblings, yet psychological support for them is limited. The long-term effects of cancer on siblings and their adjustment to non-normative events require further investigation. This study aimed to explore the needs of siblings of young people with cancer in the Quebec context. Thematic analysis of qualitative interviews revealed six primary needs of siblings of young people with cancer: attention and acknowledgment, emotional support, medical knowledge and preparatory information, inclusion, nurturing family relationships, and instrumental support. Addressing these needs through improved family functioning and tailored interventions can better support siblings throughout and after the cancer experience. The findings of this study contribute to the existing literature and provide insights for healthcare professionals, educators, and parents to offer appropriate support to siblings of young people with cancer during the cancer journey.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".