Adapting the McMaster-Ottawa scale and developing behavioral anchors for assessing performance in an interprofessional Team Observed Structured Clinical Encounter
Bibliographic record
Abstract
BACKGROUND: Current scales for interprofessional team performance do not provide adequate behavioral anchors for performance evaluation. The Team Observed Structured Clinical Encounter (TOSCE) provides an opportunity to adapt and develop an existing scale for this purpose. We aimed to test the feasibility of using a retooled scale to rate performance in a standardized patient encounter and to assess faculty ability to accurately rate both individual students and teams. METHODS: The 9-point McMaster-Ottawa Scale developed for a TOSCE was converted to a 3-point scale with behavioral anchors. Students from four professions were trained a priori to perform in teams of four at three different levels as individuals and teams. Blinded faculty raters were trained to use the scale to evaluate individual and team performances. G-theory was used to analyze ability of faculty to accurately rate individual students and teams using the retooled scale. RESULTS: Sixteen faculty, in groups of four, rated four student teams, each participating in the same TOSCE station. Faculty expressed comfort rating up to four students in a team within a 35-min timeframe. Accuracy of faculty raters varied (38-81% individuals, 50-100% teams), with errors in the direction of over-rating individual, but not team performance. There was no consistent pattern of error for raters. CONCLUSION: The TOSCE can be administered as an evaluation method for interprofessional teams. However, faculty demonstrate a 'leniency error' in rating students, even with prior training using behavioral anchors. To improve consistency, we recommend two trained faculty raters per station.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".