Promoting Interprofessional Collaboration: A Pilot Project Using Simulation in the Virtual World of Second Life
Bibliographic record
Abstract
Background: Contemporary health services increasingly call for teamwork and interprofessional collaboration, though undergraduate curricula provide few opportunities for students to develop the necessary skills. This article presents the results of an innovative pilot project focusing on providing an interprofessional clinical learning experience for students using the virtual world of Second Life.Methods and Findings: A pilot project was implemented and tested on a small group of students studying at two institutions in four healthcare programs. Qualitative descriptive methods were employed to analyze semi-structured interview transcripts. The evaluation revealed that participants were easily able to manage the technologies associated with Second Life and the learning and teaching strategies were engaging and useful. While the project provided students with an opportunity to learn more about the role of other health professionals and their contribution to patient care, it will require some development before it achieves in full the aim to promote interprofessional collaboration. Conclusions: Simulation in virtual worlds such as Second Life offers promise in the area of interprofessional 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".