Is there a magic formula? Impact of team size and composition on students' interprofessional socialization and team performance
Bibliographic record
Abstract
INTRODUCTION: Team size and composition may influence learning during interprofessional education events. This cohort study investigated the impact of these factors on changes in student interprofessional values, as well as standardized patients' (SPs) and students' perceptions of team performance. METHODS: Students from seven health professions participated in an interprofessional telehealth team visit (IPTTV) to develop a unified care plan. Pre-post changes in Interprofessional Socialization and Valuing Scale (ISVS) scores, and post-IPTTV assessments using the Modified McMaster Ottawa tool, Gap Kalamazoo tool, and the Blau heterogeneity index were analyzed. Wilcoxon signed-rank test compared within discipline changes. Kruskal-Wallis or Mann-Whitney U tests compared data between groups. RESULTS: Among 828 participating students, 447 had matched pre-post ISVS data and 661 completed evaluations of their team's performance. Team size ranged from 3 to 7 students involving 3-5 disciplines. Students demonstrated improvement in ISVS scores (+0.59, p < 0.0001). Team size (p = 0.93), the number of disciplines (p = 0.44), or the Blau heterogeneity index (p = 0.43) did not influence ISVS scores. Team size (p = 0.0003) and number of disciplines (p = 0.0003) were found to influence students' perceptions of their own team's performance. SPs' perceptions of team performance were not influenced by team size (p = 0.40) or the number of disciplines on the team (p = 0.65). CONCLUSION: Across the observed ranges of team sizes and compositions, students showed improvement in interprofessional values. Neither team size nor the number of disciplines on a team impacted the magnitude of ISVS change or SPs' perceptions of team performance, but these factors did influence students' perceptions of their own teams' performance.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".