Interprofessional Collaborator Curriculum
Bibliographic record
Abstract
Background: Physicians are often expected to participate with teams of health professionals; however, postgraduate training infrequently includes interprofessional (IP) or team training. Purpose: This curriculum was developed to demonstrate the knowledge, skills and attitudes which lead to successful IP collaboration. Curriculum: During a four-week geriatrics rotation, medicine interns complete a fifty-minute, in-person, multimedia lecture to introduce the IP collaborator concept and the Canadian and American IP competency frameworks. The IP pocket card is demonstrated and interns complete a guided, team-meeting video observation exercise. Using a Survey Monkey, narrative reporting tool, interns analyze team competencies that they observe or initiate during geriatrics team meetings during the rotation. They report on two interactions. They complete a closing Survey Monkey questionnaire and have an in-person debriefing. Results: We will have quantitative and qualitative data on interns’ recognition of IP collaborator competencies. Conclusion: Recognition of IP collaborator competencies will provide a framework for improving health professional effectiveness for systems-based care. Relevance to IP education or practice: Disseminating IP competencies. Learning Objectives: 1. The audience will be able to describe a new strategy for teaching IP competencies to health professionals. 2. The audience will become aware of a new method for combining the Canadian and American IP competencies. Todd James, MD, FACP Assistant Professor of Clinical Medicine Indiana University School of Medicine, Geriatrics Faculty Office Building, Floor 2 720 Eskenazi Avenue Indianapolis, IN 46202 Phone: 317-880-6582 Fax: 317-880-0332 Email: tojames@iu.edu
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".