Fostering Interprofessional Learning in a Rehabilitation Setting: Development of an Interprofessional Clinical Learning Unit
Bibliographic record
Abstract
PURPOSE: The development and implementation of interprofessional (IP) clinical learning units as a method to enhance IP clinical education and improve patient care in a rehabilitation setting are described. METHODS: Using a community-based participatory research approach, academia and healthcare delivery agencies formed a partnership to create an IP clinical learning unit in a rehabilitation setting. Preimplementation data from surveys and focus group data identified areas for improvement to enhance IP understanding and collaboration. A working group developed and implemented initiatives to enhance IP practice. FINDINGS: Preimplementation, eight themes emerged from which the working group identified goals and implemented strategies to strengthen IP learning. Goals included Creation of an IP Learning Environment, Increased Awareness of IP Practice, Role Clarification, Enhanced IP Communication, and Reflection and Evaluation. Postimplementation data revealed six themes: Communication, Informal IP Learning, Role Awareness, Positive Learning Environment, Logistics, and Challenges. CONCLUSIONS: The development of the IP clinical learning unit was successful and rewarding, but not without its challenges. Formal IP education was necessary to enhance collaborative practice, even in a multidisciplinary environment. Commitment and support from all participants, particularly managers and administrators from the healthcare agency, were critical to success. CLINICAL RELEVANCE: The focus of this unit was on a stroke rehabilitation unit; however, the development and implementation principles identified may be applicable to any team-based clinical setting.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".