Evaluating group dynamics through peer assessment during a global student collaboration of interprofessional healthcare education: A longitudinal study across 33 universities
Bibliographic record
Abstract
With the advent of healthcare globalization, interprofessional collaboration has become increasingly important on an international scale. This longitudinal study evaluated group dynamics in the International Collaboration and Exchange Program, a global online program of students across 33 universities from diverse healthcare backgrounds, including medicine, dentistry, pharmacy, and biomedical science. In groups of 4 to 6, participants engaged in regular discussions and projects relating to anatomy education and global health. Peer assessment was used to determine (1) whether a relationship existed between group cohesiveness and disparities in individual contribution levels and (2) whether group cohesiveness and individual contribution levels changed over time across varying group sizes. Two student cohorts were studied using the Individual Peer Assessment of Contribution methodology. Peer assessment surveys were distributed at two time points for the first (2021-2022) and second (2022-2023) cohorts, respectively, yielding 423 responses from 126 groups. Collaboration quality and effectiveness were evaluated through numerical ratings and qualitative feedback. Peer assessment is a viable tool for evaluating the dynamics of group interactions in virtual collaboration on a global scale. A reduction in group cohesiveness was associated with greater imbalances in individual contribution levels (r = -0.71, p < 0.001). Furthermore, larger groups (n = 6 students) demonstrated improved cohesiveness and equality in individual contribution levels over time compared to smaller groups (n = 4 students). This study on international healthcare student collaboration provides insights into sociocultural and educational factors impacting virtual group interactions and offers strategies for enhancing interprofessional collaborative practices in global health education.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".