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Record W4413826478 · doi:10.1177/23743735251371781

Transplant Journeys in Canada: A Cross-Sectional Survey of Transplant Patients, Caregivers, and Donors

2025· article· en· W4413826478 on OpenAlexafffundabout
Marc Hall, Arfan R. Afzal, Danielle E. Fox, Carrie Thibodeau, Lydia Lauder, Kristi Coldwell, Sandra Davidson, Sarah Dewell

Bibliographic record

VenueJournal of Patient Experience · 2025
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsThompson Rivers UniversityKidney Foundation of CanadaUniversity of Calgary
FundersHealth Canada
KeywordsCross-sectional studyOrgan donationLogistic regressionMedicineFamily medicineHealth careDescriptive statisticsDonationTransplantationPsychologySurgeryInternal medicinePathology

Abstract

fetched live from OpenAlex

Those on transplant journeys must try to understand and navigate a complex healthcare system. Little is known about whether the challenges they face differ based on their individual characteristics. This study was done to understand the experiences of those on transplant journeys in Canada. Using an online 57-question cross-sectional survey developed in collaboration with a patient-advisory committee, data was captured on transplant patients, caregivers, and living donors (n = 935). Descriptive statistics and logistic regression analyses are reported. This article includes analyses not previously reported from this larger mixed-methods project. Most participants were female (70.1%), English speaking (92.6%), and white (87.8%). The top 3 concerns identified by participants included financial (42.6%), coordination of care (37.7%), and mental health (37.2%), which affected journey types, organ types, and sociodemographic groups differently. Understanding the perspectives of those going through transplant journeys is critical to inform system change. Further, identifying how individual characteristics influence transplant journeys is essential to person-centered care that creates a lasting impact on the organ donation and transplant system and health 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.265
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes3
Has abstractyes

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