Unpacking the Role of Virtual Exchange in Promoting Student Well-being
Bibliographic record
Abstract
Educational institutions and workplaces are grappling with the task of promoting the well-being of their students and employees. From 2021 to 2023, students from Brazil, Canada, Hong Kong, Mexico, The Netherlands, and the US participated in virtual exchange collaborations in the form of global virtual teams (GVTs) and were assessed pre (n=201) and post (n=251) their GVTs experience. We used structural equation modeling (SEM) to assess changes in learner well-being from participation in GVTs. Based on the impact to student perceived well-being from the GVT experience, we found important results that can be useful for both employers and academic institutions alike. Results showed that in the pre-GVT experience, an increase in resilience led to an increase in self-reported well-being. The pathway for learners was through their greater willingness to engage, leading to more self-efficacy. The greater self-efficacy led to a heightened resilience, and greater feelings of well-being for the learner. Post-GVT experience, greater resilience led to stronger feelings of well-being which meant that these pathways to improved mental health were experienced by learners regardless of cultural identity pre or post the GVT experience.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".