Gaming the System? A Qualitative Exploration of Physician Assistant Learner Perceptions of Virtual Patient Education
Bibliographic record
Abstract
INTRODUCTION: Virtual patients (VPs) are increasingly used in health professions education. How learners engage with VPs and the relationship between engagement and authenticity is not well understood. We explored learners' perceptions of VP education to gain an understanding of the characteristics promoting meaningful engagement in learning, including perceived authenticity. METHODS: Using a constructivist grounded theory approach, we conducted interviews and focus groups with 11 students from 2 Canadian Physician Assistant programs, where VP learning was implemented to supplement clinical education during the COVID-19 pandemic. We explored trainee perspectives on the use of VPs as an educational modality. Data were iteratively collected and descriptively analyzed thematically using a constant comparison approach until theoretical sufficiency was reached. RESULTS: We identified 3 groups of factors influencing these students' VP learning experiences: (1) technical factors related to the VP platform influenced the perceived authenticity of the patient interactions; (2) individual factors of learners' attitudes influenced their engagement and motivation; and (3) contextual factors related to the learning environment influenced the acceptability and perceived value of the learning experience. Overall, the psychological authenticity of the learning platform and students' motivation for self-directed learning were perceived as most important for students' learning experiences. CONCLUSIONS: Implementing VP learning as a supplement to clinical education should be done with consideration of factors that enhance the psychological authenticity of the learning platform, promote learner engagement and accountability, and encourage acceptability of the learning modality through curricular placement and messaging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".