Role of conceptual models in a physical therapy curriculum: Application of an integrated model of theory, research, and clinical practice
Bibliographic record
Abstract
The Department of Physical Therapy, University of Alberta, Edmonton, Alberta, Canada, recently implemented a Master of Physical Therapy (MPT) entry-level degree program. As part of the curriculum design, two models were developed, a Model of Best Practice and the Clinical Decision-Making Model. Both models incorporate four key concepts of the new curriculum: 1) the concept that theory, research, and clinical practice are interdependent and inform each other; 2) the importance of client-centered practice; 3) the terminology and philosophical framework of the World Health Organization's International Classification of Functioning, Disability, and Health; and 4) the importance of evidence-based practice. In this article the general purposes of models for learning are described; the two models developed for the MPT program are described; and examples of their use with curriculum design and teaching are provided. Our experiences with both the development and use of models of practice have been positive. The models have provided both faculty and students with a simple, systematic structured framework to organize teaching and learning in the MPT program.
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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 | 0.040 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| 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".