The experiences of patients, caregivers and donors on transplant journeys in Canada: A convergent parallel mixed methods study
Bibliographic record
Abstract
INTRODUCTION: The organ donation and transplantation (ODT) system in Canada is complex and can be challenging for individuals to navigate. We thus aimed to illuminate the experiences of individuals on transplant journeys using a patient-oriented convergent parallel mixed-methods approach. METHODS: We captured data on adult patients, living donors, and caregivers on transplant journeys across Canada through an online survey (n = 935) and focus groups (n = 21). The survey was comprised of 48 questions about the individuals' experiences with the living donation and transplantation system, which were analyzed descriptively. Qualitative data were analyzed using an inductive conventional content analysis approach. RESULTS: Most participants were female (70.1%), English speaking (92.6%) and White (87.8%). Participants' experiences were represented across six key themes: holistic person-centred care, accountable care, collective impact, navigating uncertainty, connection and advocacy. Quantitative and qualitative data were integrated to identify five opportunities to improve the organ donation and transplantation system in Canada: enhancing mental health support, establishing formal peer support programmes, improving continuity of care, improving knowledge acquisition, and expanding resources and support. CONCLUSION: It is imperative that the ODT system commits to asking, listening, and learning from individuals on transplant journeys and to provide them opportunities to help improve it.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".