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.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".