Health-Related Quality of Life after Pediatric Liver Transplantation: A Qualitative Analysis of the Perspectives of Health Care Providers
Bibliographic record
Abstract
With improved survival outcomes after pediatric liver transplantation (LT), health-related quality of life (HRQoL) is an important outcome metric. Understanding the elements contributing to HRQoL after LT in children would enable more targeted strategies towards optimizing best outcomes. This qualitative study aimed to explore health care providers (HCP) perceptions about HRQoL after pediatric LT. Thirteen experienced HCP participated in two focus group discussions. Data analysis via a thematic analysis approach revealed 4 major themes: "LT as a facilitator of better HRQoL," "coping and adapting to LT," "living with a transplanted liver," and "the family context." HCP identified elements that both enhance (improved physical health, peer relationship, and activities of daily living) and challenge (need for immunosuppression, transplant follow-up, and restrictions) the multidimensional domains of HRQoL. HCP perceived LT to be a stressful life-changing event for children and their families. Patients and their parents' ability to cope and adjust positively to LT was perceived as a key contributor to better HRQoL. HCP perspective highlights the importance of promoting psychosocial support and a family-centered care delivery model towards the overarching goal of optimizing durable outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".