Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.012 |
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 it