Pharmacy Students’ Experiences of Self-regulated Learning through Simulated Virtual Patients
Bibliographic record
Abstract
Objective: Virtual patient (VP) cases are a valuable learning tool for students, used to apply classroom knowledge and develop clinical skills. It is unknown whether exposure to multiple VP cases helps students develop self-regulated learning (SRL). We sought to learn more about how students engaged in SRL as they made goals for approaching patient care during repeated exposure to cases. Methods: Second-year students (N=211) were invited to participate in an online survey. Students were surveyed before and/or after completing three VP cases. Each survey consisted of two open-ended questions. Prior to each case, students were asked “How will you change the sequence of your approach to completing the VP assessment today, if at all?” and after each case, “What more do you have to learn in order to approach similar real-life patient assessments?” A thematic analysis was conducted on open-ended survey responses. Results: One hundred and seventy pre-case and 242 post-case responses were received. The most common themes identified in pre-case surveys were a need for a more systematic approach and specific strategies for executing the patient care process. Some students had no plans for approaching VP cases. The most common themes identified in post-case surveys were knowledge gaps of medical conditions, therapeutics, and lab tests. Conclusion: VPs provided students the opportunity to self-identify gaps in knowledge and plan to strengthen their clinical reasoning skills. More research is needed to understand the relationship between VP cases, instructional guidance for supporting SRL and the realities of the intended benefits to students' learning and practice.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".