Interprofessional Team Reasoning Framework as a Tool for Case Study Analysis with Health Professions Students: A Randomized Study
Bibliographic record
Abstract
Background: This pilot study evaluated the efficacy of the Interprofessional Team Reasoning Framework (IPTRF) to facilitate teaching and learning case studies with health professions students.Methods and Findings: Eighteen interprofessional students were randomized to teams of six and were videotaped while completing a case. Team 1 (control) received only the case; team 2 received the case plus framework; and team 3 received the case, framework, and was shown videotaped examples of interprofessional interactions. The primary endpoint was students’ perceptions of interprofessional skills as measured pre and post intervention using a modified Team Skills Scale. The secondary endpoint was student performance as assessed by blinded individuals using a standardized rubric. The results revealed that students’ perceptions of team skills were significantly improved in team 2 and team 3 but not team 1. Students’ performance of their case as assessed by blinded faculty was significantly better in team 3 compared with teams 1 and 2.Conclusions: In this study of six disciplines, the IPTRF, in combination with modeled examples of interprofessional communication, was an effective tool to teach skills necessary to workup a patient case, which included collaboration, communication, and values/ethics. As the landscape of interprofessional education evolves, tools like the IPTRF will facilitate incorporation of these skills into health professions education.
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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 | 0.041 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".