Growth in the face of overwhelming pressure: A narrative review of sibling donor experiences in pediatric hematopoietic stem cell transplant
Bibliographic record
Abstract
Sibling donation in pediatric hematopoietic stem cell transplant (HSCT) can be emotionally distressing for children, but may simultaneously evoke positive emotions, and has the potential to facilitate personal growth. We conducted a narrative review of sibling donor experiences, which included an analysis of psychosocial distress and post-traumatic growth (PTG). We searched the following databases: MEDLINE, CINAHL, PsycInfo, and SCOPUS. Search concepts used to develop key terms included HSCT, siblings, children, and psychosocial outcomes. Specific inclusion criteria included a) research articles published in English in peer-reviewed journals until September 2020, and b) reported trauma symptoms and PTG characteristics of sibling donation experiences. Four themes were identified: fear and anxiety related to HLA testing, overwhelming pressure to donate, guilt and blame when the ill child died, as well as emotional and physical isolation following donation. Sibling responses also included evidence of PTG, articulated as a deepened appreciation for life, closer relationships with the ill child and other family members, increased personal strength, and spiritual growth. These results highlight a critical need for future research approaches that further empower sibling donor voices, such as those found in participatory, arts-based methodologies.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".