Evaluating Interprofessional Team Performance: A Faculty Rater Tool
Bibliographic record
Abstract
INTRODUCTION: Reliable team assessment has become a priority because of growing emphasis on interprofessional education and team-based care. Objective rating scales are needed to evaluate interprofessional student teams and individuals and provide real-time feedback. METHODS: In response to a need for behavioral rating scales, we modified the McMaster-Ottawa Scale from a 9-point to a 3-point scale and added descriptive behavioral anchors to define three levels of competency (i.e., below, at, and above expected). This modification is intended to provide consistent rating of individuals and teams in patient settings. We then developed a demonstration video using actors representing four professions to demonstrate the three levels of performance within the team. Our faculty rater tool, consisting of the modified scale and video, is designed to provide standardized ratings in interprofessional educational settings that involve patient care. RESULTS: We conducted training sessions with 40 faculty members from seven professions (medicine, dentistry, occupational therapy, nursing, pharmacy, physician assistant, and psychology) over a 2-year period. Immediately after each training session, two trained faculty observers rated interprofessional student teams as they conducted history and assessments on standardized patients. Observer scores were compared with one another and with standard expert ratings of the same teams. Trained observer ratings were consistent across the pairs. The observer training can be conducted within 60-90 minutes with the tool. DISCUSSION: Results of our implementation of the faculty rater tool confirm that the modified McMaster-Ottawa Scale is feasible to administer in clinical settings and that the demonstration video can be easily adopted for standardizing observer ratings.
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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 | 0.068 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".