Explore Transplant Ontario: Adapting the Explore Transplant Education Program to Facilitate Informed Decision Making About Kidney Transplantation
Bibliographic record
Abstract
PURPOSE: In this article, we describe a province-wide collaborative project in which we adapted the Explore Transplant (ET) education program for use in Ontario, Canada, to develop Explore Transplant Ontario (ETO). Kidney transplantation (KT), especially living donor kidney transplantation (LDKT), is the best treatment for many patients with end-stage kidney disease (ESKD), with the best patient survival and quality of life and also reduced health care costs. Yet KT and LDKT are underutilized both internationally and in Canada. Research has demonstrated that patients with ESKD who receive personalized transplant education are more likely to complete the transplant evaluation process and to receive LDKT compared with patients who do not receive this education. SOURCES OF INFORMATION: Research expertise of the lead authors and Medline search of studies assessing the impact of education interventions on access to KT and LDKT. METHODS: The ET program, developed by Dr Amy Waterman, has been used in thousands of patients with ESKD in the United States to enhance KT and LDKT knowledge. To adapt this program for use in Ontario, we convened a working group, including patient representatives, nephrologists, transplant coordinators, dialysis nurses, and patient educators from all Ontario KT centers and selected dialysis units. In an iterative process concluding in a consensus workshop, the working group reviewed and edited the text of the original ET program and suggested changes to the videos. KEY FINDINGS: The adapted program reflects the Ontario health care environment and responds to the specific needs of patients with chronic kidney disease (CKD) in the province. The videos feature Ontario transplant nephrologists, transplant coordinators, and patients, representative of the ethnic diversity in Ontario, sharing their transplant experience and expertise. Despite the changes, ETO is consistent with the quality and style of the original ET program. At the end of this article, we summarize subsequent steps to test and utilize ETO. Those projects, specifically the ETO pilot study and a multicomponent quality improvement initiative to increase utilization of KT and LDKT across Ontario, will be described in full in future papers. LIMITATIONS: This article describes a provincial initiative; therefore, our findings may not be fully generalizable without further considerations. The adapted education program has not yet been tested in large trial for effectiveness. IMPLICATIONS: As a program grounded in the theoretical model of behavior change, ETO places patients with ESKD at the center of a complex process of navigating renal replacement therapy modalities and acknowledges a broad range of patient values, priorities, and states of readiness to pursue KT.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".