Developing virtual immersive simulations to promote practice readiness for pharmacy and pharmacy technician students
Bibliographic record
Abstract
BACKGROUND: Although registered pharmacists (RPhs) and registered pharmacy technicians (RPhTs) are expected to work collaboratively, as students they have limited opportunities to learn together. The objective of this project was to provide a way to mitigate a gap in students' understanding of intraprofessional collaboration between RPhs and RPhTs in pharmacy practice settings. EDUCATIONAL ACTIVITY: Three virtual pharmacy simulations were developed in collaboration with RPhs, RPhTs, and the research team using Twine® software and have been made available for use or adaptation by other educators. The simulations were administered to pharmacy and pharmacy technician students as part of course work. Simulation topics focused on patient inquiries related to selecting cold medication, vaccines, and filling a prescription. EVALUATION FINDINGS: Pharmacy students completed pre- (n = 187) and post-simulation (n = 185) surveys. Students reported an increase in comprehension of RPh and RPhT roles and increased confidence in ability to collaborate. Post-simulation, 88.8 % of respondents agreed or strongly agreed they were confident about applying scopes of practice in an intraprofessional setting, and 96.9 % of respondents agreed or strongly agreed they felt more confident collaborating with RPhTs. Themes emerging from the open-ended survey question were Simulation Strengths, Areas for Improvement, Scope of Practice, Inter- and Intra-Professional Collaboration, and Knowledge Application. ANALYSIS OF EDUCATIONAL ACTIVITY: Through this project we learned that the development of virtual immersive simulations can be time and resource intensive. For future projects, we aim to understand how virtual simulations can most effectively be developed and used to support pharmacy student learning.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".