Patient-reported outcome measures in pediatric solid organ transplantation: Exploring stakeholder perspectives on clinical implementation through qualitative description
Bibliographic record
Abstract
PURPOSE: Patient-reported outcome measures (PROMs) are standardized instruments used to collect data about the subjective assessment of medical care from the patient perspective. Implementing PROMs within pediatric clinical settings has gained increasing importance as health services prioritize patient-centred pediatric care. This study explores the perspectives of pediatric solid organ transplant patients, caregivers, and healthcare practitioners (HCPs) on implementing PROMs into clinical practice. METHODS: Qualitative description methods were used to elicit stakeholder perspectives. Semi-structured interviews were conducted across five Canadian transplant centres. Purposive sampling was used to obtain maximum variation across age, gender, and transplant program for all participants, as well as discipline for HCPs. RESULTS: The study included a total of 63 participants [patients (n = 20), caregivers (n = 22) and HCPs (n = 21)]. Nearly all participants endorsed the implementation of PROMs to enhance pediatric transplant clinical care. Three primary roles for PROMs emerged: (1) to bring a transplant patient's overall well-being into the clinical care conversation; (2) to improve patient communication and engagement; and, (3) to inform the practice of clinical pediatric transplant care. Insights for effective implementation included completing electronic PROMs remotely and prior to clinical appointments by patients who are eight to 10 years of age or older. CONCLUSIONS: This study contributes to current research that supports the use of PROMs in clinical pediatric care and guides their effective implementation into practice. Future directions include the development, usability testing, and evaluation of a proposed electronic PROM platform that will inform future research initiatives.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.115 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".