Development of a digital learning program for physiotherapists to enhance clinical implementation of aerobic exercise in stroke rehabilitation
Bibliographic record
Abstract
BACKGROUND: This paper describes the initial development process of an eLearning continuing professional education program primarily for post-licensure physiotherapists -"Electronic Aerobic Exercise Recommendations to Optimize Best Practices in Care after Stroke" (eAEROBICS). Our objective was to develop an evidence-based, clinically relevant, user-friendly eLearning program for online delivery tailored to facilitate prescription of aerobic exercise post-stroke by physiotherapists. The Demand Driven Learning Model guided curriculum design, delivery, and evaluation. Based on previously identified gaps in physiotherapists' knowledge of aerobic exercise, four learning modules were developed and delivered using an eLearning platform to maximize cost-effectiveness and flexibility. Five physiotherapists volunteered to pilot eAEROBICS, providing preliminary feedback on strengths and suggestions for improvement. RESULTS: Theoretical information and clinical applications addressed the learning objectives of each module in a logical manner. All technical or administrative issues encountered during program delivery were addressed. The feedback from the pilot end-users informed modifications to the eAEROBICS program. CONCLUSIONS: Processes used in developing eAEROBICS have the potential to serve as a model of electronic continuing professional education for other areas of physiotherapy practice. Further investigation of end-user perspectives and clinical impact of the program is warranted to determine the overall effectiveness of the program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".