Student perceptions of hybrid delivery of interprofessional anatomy—The best of both worlds?
Bibliographic record
Abstract
Interprofessional anatomy dissection (IAD) courses increase students' readiness for interprofessional education (IPE) both in-person and online. During the COVID-19 pandemic, virtual environments for anatomy learning were perceived as less effective. Hybrid instruction approaches emerged but have been scarcely evaluated. This study assessed students' experiences with a hybrid IAD course's virtual and in-person components. A hybrid IAD course consisting of virtual and in-person anatomy laboratory-based instruction was offered to 32 students from different health sciences programs. Before and after the full course, students completed the Readiness for Interprofessional Learning Scale (RIPLS) and the Interdisciplinary Education Perception Scale (IEPS). After the virtual and the in-person course components, students completed a Q-methodology survey to assess their perceptions of the course. Twenty-eight students (20 females; 24.8 ± 6.3 years old) from different programs (4 Physician Assistant; 2 Midwifery; 3 Speech-Language Pathology; 4 Physiotherapy; 3 Occupational therapy; 4 Nursing; 8 Medicine) participated. The total RIPLS score improved after the 8-week course (Median 84 interquartile range [78-87] vs. 87 [85-90]; p = 0.0145). The Q-methodology identified three factors: IPE & Virtual Enthusiasts, Introspective Learners, and IPE & Virtual Skeptics. Factors represented different levels of students' engagement with the IPE and virtual environment. The transition to in-person resulted in all factors praising the experience. Health science students showed improvements in their readiness for IPE after an 8-week hybrid IAD course. The main differences in the evaluations of the virtual and in-person components were related to engagement and the ability to learn anatomy; no differences were noted between settings regarding engagement in IPE.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".