When less is more: validating a brief scale to rate interprofessional team competencies
Bibliographic record
Abstract
BACKGROUND: There is a need for validated and easy-to-apply behavior-based tools for assessing interprofessional team competencies in clinical settings. The seven-item observer-based Modified McMaster-Ottawa scale was developed for the Team Objective Structured Clinical Encounter (TOSCE) to assess individual and team performance in interprofessional patient encounters. OBJECTIVE: We aimed to improve scale usability for clinical settings by reducing item numbers while maintaining generalizability; and to explore the minimum number of observed cases required to achieve modest generalizability for giving feedback. DESIGN: We administered a two-station TOSCE in April 2016 to 63 students split into 16 newly-formed teams, each consisting of four professions. The stations were of similar difficulty. We trained sixteen faculty to rate two teams each. We examined individual and team performance scores using generalizability (G) theory and principal component analysis (PCA). RESULTS: The seven-item scale shows modest generalizability (.75) with individual scores. PCA revealed multicollinearity and singularity among scale items and we identified three potential items for removal. Reducing items for individual scores from seven to four (measuring Collaboration, Roles, Patient/Family-centeredness, and Conflict Management) changed scale generalizability from .75 to .73. Performance assessment with two cases is associated with reasonable generalizability (.73). Students in newly-formed interprofessional teams show a learning curve after one patient encounter. Team scores from a two-station TOSCE demonstrate low generalizability whether the scale consisted of four (.53) or seven items (.55). CONCLUSION: The four-item Modified McMaster-Ottawa scale for assessing individual performance in interprofessional teams retains the generalizability and validity of the seven-item scale. Observation of students in teams interacting with two different patients provides reasonably reliable ratings for giving feedback. The four-item scale has potential for assessing individual student skills and the impact of IPE curricula in clinical practice settings. ABBREVIATIONS: IPE: Interprofessional education; SP: Standardized patient; TOSCE: Team objective structured clinical encounter.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".