The Effects of Intraprofessional Collaborative Case Based Learning: A Cohort Study of Student Physical Therapist and Physical Therapist Assistant Perceptions of the Physical Therapist Role
Bibliographic record
Abstract
Background: The changing nature and complex regulation of healthcare require the efficient use of resources, including the appropriate delegation and supervision of the physical therapist assistant (PTA). Knowledge of the scope of PTA practice introduced in the academic curriculum is mandated for entry-level practice. This study assessed the effect of a collaborative case-based educational intervention within the didactic curriculums of a physical therapy (PT) and PTA program on student knowledge of PTA scope of practice.Methods and Findings: A pre- and post-test research design was used. Students completed a validated survey exploring their perceptions of the PTA role before beginning the case study. The case study was a classroom assignment followed by instructional prompts requiring interactions between student cohorts three times over four weeks. Following case study completion, students completed the same survey. Independent and paired samples t-tests detected significant differences between and within groups (p < .05).Conclusions: Based on the results, the case-based instructional model was efficacious in teaching both student cohorts about the role of the PTA. The impact was greater on the accuracy of the PT students, but PTA students became less uncertain in their perceptions. The effect of the clinical learning environment should be investigated to determine the impact on student perception of PTA role delineation following didactic instruction.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".