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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".