322.1: Education in donation of organs for transplants (EDOT): A novel certificate to strengthen Canada’s organ and tissue donation coordinator workforce.
Bibliographic record
Abstract
Introduction: To date, Canada lacks a standardized training for organ and tissue donation coordinators. Our previous national led study, found this absence to be linked to increased stress levels and burnout among Canadian coordinators. In response to coordinators’ concerns, our research team is developing and testing the feasibility of a certificate course to support coordinators’ practice and well-being, and ultimately, improve the quality of deceased organ donation processes. Methods: This is a multi-phase and multi-method project (Figure1), which includes 1) development of an evidence-based course curriculum (Delphi technique with experts and key stakeholders), 2) the development of course content, logistics and course delivery (scoping review of educational methods, selection of lecturers and logistical organization) 3) Course evaluation (quantitative and qualitative methods) and sustainability plan through cost-benefit analysis and course accreditation through the Canadian Nurses Association. Preliminary Results: We have completed a scoping review in educational methods in which we identified that educational strategies for coordinators are delivered in diverse formats and have mixed results in terms of their impact on professional practice and patient outcomes. Furthermore, comprehensive program evaluation and evidence-based decision making are hindered by the lack of standardization in training content, the extent of the content, and minimal or non-existent cost reporting. These findings support the need for the development of an evidence driven training program with integrated evaluation, hence the creation and implementation of our virtual pilot simulation is undergoing. This project is currently in progress, and we will present preliminary results from completed phases, along with any updates to the curriculum design at ISODP 2025. Conclusions: Through the implementation of our novel education strategy, we aim to enhance the quality, consistency, and efficiency of deceased organ donation by equipping coordinators with essential knowledge and support. By fostering their professional satisfaction, resilience, and well-being, we anticipate improved decision-making, communication, and coordination—ultimately leading to better donor management and increased organ donation outcomes.We would like to thank the Canadian Alliance of Nurse Educators Using Simulation that have funded a pilot virtual simulation game that is part of the development of this certificate course. We also thank the Organ Donation Organizations in Canada who have kindly reviewed some of the content for this course and are solid partners supporting this work. We thank the Canadian Nurses Association and the Canadian Association of Critical Care Nurses for their ongoing support as well.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".