Inter-professional Education in the Acute-Care Setting: The Clinical Instructor's Point of View
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to gain an understanding of the opportunities and challenges involved in providing clinical inter-professional education (IPE) to physical therapy (PT) students in the acute-care setting from the perspective of PT clinical instructors (CIs). METHODS: Focus groups were conducted in four acute-care hospitals in Toronto. Participants were recruited using a purposive and convenience sampling approach in order to enhance our understanding of the perceptions of acute-care PT CIs. Eighteen full-time PT CIs with an average of 11 years in practice participated. A constant comparative process was employed to identify recurrent issues and themes within and between groups. RESULTS: Three main themes emerged from the focus groups: (1) Clinical IPE happens when inter-professional collaboration (IPC) occurs; however, IPC differs according to setting, access to other professions, time, support, and structure. (2) IPE is a lifelong learning process that applies to both CIs and students. (3) Student preparedness is a prerequisite for clinical IPE. CONCLUSIONS: IPC is an integral part of clinical IPE that requires ongoing commitment and reflection by CIs to ensure that they are ready to instruct students who have some preparation in formal IPE. More knowledge about providing clinical IPE in a structured manner, through academic and health institutions, will allow CIs to become role models for future generations of PT students.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".