Strategies Proposed by Students and Pharmacists for Virtual Experiential Patient Care Practicums
Bibliographic record
Abstract
Background: The University of British Columbia (UBC) Pharmacists Clinic (the Clinic) is a pharmacist-led patient care clinic serving as a practice site for experiential education in a team-based primary care practice. Given the unprecedented circumstances surrounding COVID-19, pharmacy practice sites have transitioned some of their experiential education activities to a virtual format. Currently, there is limited literature on developing best teaching practices which are conducive to students’ success in a virtual environment. Objective: To determine the factors that enable successful development of a virtual patient care practicum experience at a university clinic from the perspectives of student pharmacists and practice educators. Methods: A qualitative research methodology was used to gain the perspectives of student pharmacists and practice educators. Separate focus group interviews were conducted using a semi-structured approach and consisted of questions aimed at gathering insight into participant perspectives on virtual practicums. The focus group sessions were audio recorded with participant consent and transcribed. A thematic analysis was conducted to analyze the data. Results: Three pharmacist practice educators and three student pharmacists participated in their respective focus groups. A thematic analysis was used to analyze the data. Six major themes emerged: (1) technology optimization, (2) patient care related activities, (3) student-practice educator relationship, (4) student skill development, (5) student support, and (6) in-person vs virtual practicum preferences. Proposed strategies to mitigate the limitations of virtual practicums included setting communication guidelines, arranging enriching learning opportunities, and having reliable internet connection. Conclusion: The participants in this study provided insight on factors to support successful development and delivery of a virtual patient care practicum. The results from this study can be applied to other health disciplines and their approach to virtual practicums during and following the COVID-19 pandemic.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.005 |
| 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".