Integration of behavioral medicine competencies into physiotherapy curriculum in an exemplary Swedish program: rationale, process, and review
Bibliographic record
Abstract
In 2004, Mälardalen University, Sweden, introduced a new undergraduate entry-level physiotherapy program. Program developers constructed the curriculum with behavioral medicine content that reflected the contemporary definition and values of the physiotherapy profession aligning it with current best practices, evidence, and the International Classification of Functioning, Disability, and Health (ICF). The new curriculum conceptualized movement and function as modifiable behaviors in that they reflect behavioral contingencies, perceptions, beliefs, and lifestyle factors as well as pathophysiology and environmental factors. The purpose of this article is to describe how one university accordingly structured its new curriculum and its review. We describe the rationale for the curriculum's behavioral medicine content and competencies, its development and implementation, challenges, long-term outcomes, and its related research enterprise. We conclude that physiotherapy practiced by our graduates augments that taught in other programs based on accreditation reviews. With their expanded practice scope, graduates are systematically practicing within the constructs of health and function conceptualized within the ICF. Our intent in sharing our experience is to exemplify one university's initiative to best prepare students with respect to maximizing physiotherapy outcomes as well as establish a dialogue regarding minimum standards of behavioral medicine competencies in physiotherapy education and practice.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".