Implementation of Virtual Interactive Cases for Pharmacy Education: A Single-Center Experience
Bibliographic record
Abstract
Patient case simulation software are described in pharmacy education literature as useful tools to improve skills in patient assessment (including medication history-taking and physical assessment), clinical reasoning and communication, and are typically well-received by students and instructors. The virtual interactive case (VIC) system is a web-based software developed to deliver deliberate practice opportunities in simulated patient encounters across a spectrum of clinical topics. This article describes the implementation and utilization of VIC in the undergraduate curriculum at one Canadian pharmacy school. Methods: At our facility, the use of VIC was integrated across the training spectrum in the curriculum, including core and elective didactic courses and practice labs, experiential learning, interprofessional education, and continuing education. Its use was evaluated through student and instructor surveys and qualitative student interviews). VIC is easy to navigate and created a positive and realistic learning environment. Students identified that it enhanced their ability to identify relevant patient information, accurately simulated hospital pharmacy practice and thereby helped them to prepare for their upcoming experiential courses. The use of VIC has expanded beyond its original intended purpose for individual student practice to become a valuable addition to pharmacy undergraduate education. Future plans include ongoing development of cases and exploration of further uses of VIC within the didactic curriculum, for remediation in experiential courses, and for pharmacist continuing education.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".