A Novel Approach to Interprofessional Education: Interprofessional Day, the Four-Year Experience at the Medical University of South Carolina
Bibliographic record
Abstract
Background: In order to introduce students to different disciplines and promote interprofessional teamwork, the Medical University of South Carolina developed an innovative educational program, Interprofessional (IP) Day, for all first- and second-year health professions students. The IP Day Committee, composed of representatives from each of the six colleges (pharmacy, nursing, medicine, graduate studies, health professions, and dental medicine), coordinates the day's activities. The morning session (for second-year students only) and the afternoon session (for first-year students only) each begin with a large group meeting where an invited speaker details the concept and implementation of interprofessional teamwork. Following the speaker, students divide into small discussion groups containing at least one student from each of the six colleges and led by a faculty member and student facilitators. The first-year session introduces the role of each discipline (e.g., occupational therapy, nursing). The second-year session promotes teamwork among the professions via a case discussion.Methods and Findings: We assessed the students' satisfaction with the program and measured their attitudes toward interprofessional collaboration using both quantitative and qualitative methods. Results from a formative evaluation of the IP Day suggest improved knowledge about other healthcare professions after participating in interprofessional day, particularly for first-year students.Conclusions: IP Day lays the groundwork for our students' successful interprofessional collaborative experience at MUSC, a paramount university goal.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".