Piloting an interprofessional virtual cadaveric dissection course: Responding to <scp>COVID</scp>‐19
Bibliographic record
Abstract
Interprofessional learning improves students' clinical and interprofessional competencies. COVID-19 prevented delivering in-person education and motivated the development of a virtual interprofessional cadaveric dissection (ICD) course. This study reports on the effects of a virtual ICD course compared to a previously delivered in-person course, on students' readiness for, and perceptions about, interprofessional learning. Students attending the ICD course in-person (2019-2020) or virtually (2020-2021) completed the Readiness for Interprofessional Learning Scale (RIPLS) and the Interdisciplinary Education Perception Scale (IEPS). Students in the virtual course also provided written feedback. Thirty-two (24 women; Median: 24 [Q1-Q3: 22-25] years) and 23 students (18 women; 22 [21-23] years) attended the in-person and virtual courses, respectively. In the virtual cohort, the RIPLS total score (82 [76-87] vs. 85 [78-90]; p = 0.034) and the roles and responsibilities sub-score (11 [9-12] vs. 12 [11-13]; p = 0.001) improved significantly. In the in-person cohort, the roles and responsibilities sub-score improved significantly (12 [10-14] vs. 13 [11-14]; p = 0.017). No significant differences were observed between cohorts (p < 0.05). Themes identified in the qualitative analysis were advantages and positive experiences, competencies acquired, disadvantages and challenges, and preferences and suggestions. In-person and virtual ICD courses seem to have similar effects on students' interprofessional learning. However, students reported preferring the in-person setting for learning anatomy-dissection skills.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".