Medical Students’ Learning About Other Professions Using an Interprofessional Virtual Patient While Remotely Connected With a Study Group: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Collaboration with other professions is essential in health care education to prepare students for future clinical teamwork. However, health care education still struggles to incorporate interprofessional education. Distance learning and virtual patients (VPs) may be useful additional methods to increase students' possibilities for interprofessional learning. OBJECTIVE: This study had two aims. The first was to assess if an interprofessional VP case could facilitate medical students' learning about team collaboration in online groups. The second was to assess how students experienced learning with the VP when remotely connected with their group. METHODS: A mixed methods design was used. The VP case was a 73-year-old man who needed help from different health professions in his home after a hip fracture. Questionnaires were answered by the students before and directly after each session. Qualitative group interviews were performed with each group of students directly after the VP sessions, and the interviews were analyzed using qualitative content analysis. RESULTS: A total of 49 third-year medical students divided into 15 groups participated in the study. Each group had 2 to 5 students who worked together with the interprofessional VP without a teacher's guidance. In the analysis of the group interviews, a single theme was identified: the interprofessional VP promoted student interaction and gave insight into team collaboration. Two categories were found: (1) the structure of the VP facilitated students' learning and (2) students perceived the collaboration in their remotely connected groups as functioning well and being effective. The results from the questionnaires showed that the students had gained insights into the roles and competencies of other health care professions. CONCLUSIONS: This study demonstrates that an interprofessional VP enabled insights into team collaboration and increased understanding of other professions among student groups comprising only medical students. The interprofessional VP seemed to benefit students' learning in an online, remote-learning context. Although our VP was not used as an interprofessional student activity according to the common definition of interprofessional education, the results imply that it still contributed to students' interprofessional learning.
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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 | 0.005 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".