Augmented behavioral medicine competencies in physical therapy students’ clinical reasoning with a targeted curriculum: a final-semester cohort-comparison study
Bibliographic record
Abstract
BACKGROUND: Knowledge regarding the impact of curricula with behavioral medicine content and competencies (BMCC) on physical therapy (PT) students' clinical reasoning skills is lacking. OBJECTIVES: The primary objective was to compare the clinical reasoning skills, focusing on clients' behavioral change, of entry-level PT students with or without BMCC in their curricula. The secondary objective was to compare students' attitudes and beliefs in a biomedical and biopsychosocial practice orientation. METHODS: Swedish final-semester PT students (n = 151) completed the Reasoning 4 Change (R4C) instrument and the Pain Attitudes and Beliefs Scale for Physiotherapists. A blueprint was used for curricular categorization. The independent t-test was used. RESULTS: Students attending programs with BMCC curricula (n = 61) had superior scores compared with students without BMCC curricula (n = 90) in the following R4C variables, all of which were related to clinical reasoning focused on behavioral change: Knowledge, Cognition, Self-efficacy, Input from the client, Functional behavioral analysis, and Strategies for behavioral change. Students who did not receive BMCC curricula scored higher in the R4C contextual factors and reported a greater biomedical practice orientation than students receiving BMCC curricula. There was no difference in the biopsychosocial practice orientation between groups. CONCLUSIONS: Our findings support the benefit of structured entry-level PT curricula with BMCC on final-semester students' clinical reasoning skills focused on behavioral change and their level of biomedical practice orientation. Further, our findings elucidated educational opportunities to augment students' self-efficacy and strengthen their behavioral competencies in clinical reasoning. For the generalizability of the results further research in other contexts is needed.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".