Characterizing adult rehabilitation programs for solid organ transplant candidates and recipients across Canada
Bibliographic record
Abstract
Introduction: Rehabilitation is integral for solid organ transplant (SOT) candidates and recipients, and aims to build physical capacity for surgery, facilitate post-operative recovery, and mitigate long-term complications. Prior to the COVID-19 era, in-person programs were the primary delivery model in Canadian SOT rehabilitation programs, but there are several knowledge gaps with the current delivery models. The aims of this study were to: 1) assess the characteristics and current practices of SOT rehabilitation programs in Canada, and 2) identify key facilitators and barriers to providing rehabilitation for the SOT population. Methods: An electronic survey was administered to 17 adult Canadian SOT rehabilitation programs utilizing REDCap in April 2024. The survey examined types of exercise training and supervision practices, clinical outcome measures, delivery models, safety considerations, facilitators, and barriers. Survey measures were summarized using descriptive statistics. Results: The response rate was 59% (10/17). Post COVID-19, there has been a shift in program delivery, with majority (60%) of SOT rehabilitation programs now using a hybrid approach comprised of both in-person and virtual components. There is heterogeneity among programs with respect to clinical assessments, safety measures, and virtual rehabilitation platforms. The most common barriers were limitations in funding and healthcare personnel. Conclusion: This study provides a better understanding of the current landscape and variability of SOT rehabilitation programs. Most programs have transitioned to hybrid models post-COVID-19, which may facilitate greater access. Future research can leverage findings from this survey to optimize SOT rehabilitation programs and improve clinical 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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".