Increasing Understanding of Physiotherapists’ Approaches to Implementing Aerobic Exercise Testing during In-patient Stroke Rehabilitation in Canada
Bibliographic record
Abstract
Stroke clinical practice guidelines recommend aerobic exercise (AEx) testing to determine an intensity for AEx training that can safely improve cardiovascular fitness for individuals post-stroke. However, the variety of approaches to performing AEx testing and competing priorities during in-patient stroke rehabilitation can make it challenging for physiotherapists (PTs) toimplement AEx testing with individuals undergoing in-patient stroke rehabilitation. This thesis addresses the disconnect between guidelines and clinical practice with 3 studies aimed at 1)describing submaximal AEx testing protocols that are safe for people with subacute stroke, 2)describing current AEx testing practices and the barriers and facilitators to AEx testing faced by PTs working in in-patient stroke rehabilitation, and 3) exploring the experiences of PTs, who routinely perform submaximal AEx testing, with the clinical implementation of submaximal AEx testing with individuals undergoing stroke rehabilitation. Study 1, a scoping review, identified several submaximal AEx testing protocols, with conservative test termination criteria and appropriate monitoring, that were safely implemented with people with subacute stroke, including those with comorbidity. Study 2, a web-based survey, found that less than half of PTs working in in-patient stroke rehabilitation settings in Canada perform AEx testing with patients post-stroke due to a range of practitioner- and organizational-level barriers. Lastly, study 3, a qualitative descriptive study incorporating a realist approach, explored how submaximal AEx testing, using a 6MWT protocol or an incremental protocol, can be a complex process that requires PTs to have the appropriate knowledge, skills, supports, and resources to perform regularly. Overall, thesis findings increase understanding of how PTs can implement submaximal AEx testing during in-patient stroke rehabilitation, including what educational and clinical resources and supports they need to do so. Future research can build upon these findings to develop knowledge translation products and interventions that will facilitate PTs’ submaximal AEx testing practices with people undergoing in-patient stroke rehabilitation.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".