Evaluating the Educational Experience of Physiotherapy Students Using the Fitness and Mobility Exercise (FAME) Programme to Learn about Neurological Conditions: An Exploratory Study
Bibliographic record
Abstract
Purpose: Group exercise has the potential to be a cost-effective way to improve functional outcomes for those living with neurological injury. Leading group exercise is a foundational competency for entry-to-practice for physiotherapy students. The overall objective of this study was to examine the student experience of using the Fitness and Mobility Exercise (FAME) programme to learn about neurological conditions in a group setting. Methods: Sixteen physiotherapy students filled out a single point in time survey at the end of their placement during which they had the opportunity to use FAME with their clients twice a week. The survey had Likert and open-ended questions and demographic information. Likert responses were calculated as means. Open-ended questions were analyzed using thematic analysis. Results: The Likert questions were answered with almost entirely positive results. The main themes from the open-ended questions were how to personalize the class, characteristics of individual clients shape the class experience and factors that make the class successful. Conclusions: Overall, the physiotherapy students found using FAME to be a positive experience. From the student perspective, using FAME during placement was an effective way to learn about neurological conditions as well as to develop skills to manage a group exercise class.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".