Designing a Novel Interprofessional and Inter-University Education Session for Healthcare Trainees to Improve Interprofessional Practice
Bibliographic record
Abstract
Interprofessional education is widely acknowledged as critical for training successful clinicians, however logistical challenges often interfere with its implementation. The aim of this paper is to describe the procedures developed to enable students in different health professional programs in different geographic regions within the same country to learn about each other’s professions and apply this knowledge to optimize outcomes for patients. Principles from the Rehabilitation Treatment Specification System and Universal Design for Learning were combined to design an efficient and effective virtual approach to achieving interprofessional knowledge and collaborative skill outcomes. Application of these principles resulted in a 3-stage approach combining synchronous and asynchronous learning as well as didactic and problem-based learning. This paper describes the design and implementation for speech-language pathology and pharmacy students learning about swallowing disorders, but the procedures are applicable to a broad range of professions and academic content when interprofessional education is the goal.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".