Is there a magic formula? Impact of team size and composition on students' interprofessional socialization and team performance
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it