Implementing Team-Based Learning in Physiotherapy Education: Students’ Perceptions and Preferences Compared to the Traditional Lecture
Bibliographic record
Abstract
Silvia Pérez-Guillén,1 Andoni Carrasco-Uribarren,1 Euson Yeung,2 Pol Serra-Llobet,1 Pilar Pardos-Aguilella,3 Sara Cabanillas-Barea1 1Faculty of Medicine and Health Sciences, Universitat Internacional de Catalunya, Barcelona, Spain; 2Temetry Faculty of Medicine, University of Toronto, Department of Physical Therapy, Toronto, Canada; 3Faculty of Health Sciences, Universidad de Zaragoza, Zaragoza, SpainCorrespondence: Andoni Carrasco-Uribarren, Faculty of Medicine and Health Sciences, Universitat Internacional de Catalunya, Sant Cugat del Vallés, 08195, Spain, Email acarrasco@uic.esIntroduction: Team-Based Learning (TBL) is an educational strategy designed for small groups that can be effectively implemented across various educational levels. The aim of TBL is the development of meaningful learning teams, facilitating student interaction and effective communication in problem-solving. It is hypothesized that the use of TBL demonstrates higher levels of satisfaction, engagement and responsibility regarding the acquisition of knowledge than the traditional method of master class.Methods: A cross-sectional study was carried out. Twenty-four university students enrolled in the subject of Clinical Reasoning and Evidence Based Practice of the Physiotherapy Master´s programme during the academic year 2022– 23 were included. Engagement, satisfaction and preferences were collected through the TBL Student Assessment Instrument (TBL-SAI).Results: Twenty-three students were included in the final analysis, with a mean age of 25.29 ± 3.84 years. The results obtained from the TBL-SAI indicated a score of 25.57 on the accountability subscale, 51.04 on the preference for this learning approach subscale, and 32.43 on the overall satisfaction subscale.Conclusion: Students found TBL to be engaging, fostering greater responsibility for both individual and group learning. Compared to traditional lectures, TBL sessions were preferred by students, reflecting a higher level of satisfaction with this collaborative learning approach. Further investigation is warranted to assess long-term knowledge retention and to ensure alignment between TBL activities and intended learning objectives.Keywords: active methodologies, task-based learning, clinical reasoning
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".