Disseminating Knowledge to Providers on Exercise Training After Solid Organ Transplantation
Bibliographic record
Abstract
INTRODUCTION: The objectives of our dissemination project were (1) to disseminate the evidence supporting exercise training in solid organ transplantation to exercise professionals, health-care professionals, physicians, and directors of transplant programs in order to enhance their ability to apply evidence to practice and (2) to build a community of exercise professionals and researchers across Canada. METHODS: We used the 5-step Patient-Centered Outcomes Research Institute model for knowledge translation to guide our project: (1) evidence assessment, (2) audience and partner identification, (3) dissemination, (4) implementation, and (5) evaluation. After meeting with experts in the field, conducting a literature review, and identifying an appropriate audience, we took our presentations on the road across Canada. RESULTS: We visited 10 transplant centers and held interactive knowledge translation sessions in each center. To provide sustainability and to facilitate the adoption of the research evidence, we founded the Canadian Network for Rehabilitation and Exercise for Solid Organ Transplant Optimal Recovery network and created its website. CONCLUSIONS: Our project raised awareness of the importance of exercise among many health professionals in Canada and built a community of exercise professionals and researchers in the field of transplantation through the rehabilitation network. It also led to the creation of online resources that will facilitate the implementation of rehabilitation programs in transplant centers.
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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 | 0.127 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".