Bringing the Patient Voice into Workplace-Based Assessment of Pharmacy Learners: An Interpretive Description Study
Bibliographic record
Abstract
OBJECTIVES: This study sought to explore how patients view their involvement in pharmacy learner assessment by comparing and contrasting patients' and pharmacy learners' perspectives on learner skills patients are capable of providing feedback on. METHODS: We conducted a qualitative study informed by interpretive description methodology and situated in a pharmacist-led clinic that serves as a teaching site for pharmacy learners. We interviewed 10 patients who were cared for by a pharmacy learner and 10 pharmacy learners who were completing clerkship training. Data analysis was iterative and used a thematic approach. RESULTS: All patient participants expressed interest in giving feedback on pharmacy learner skills while learners regarded patient feedback as an asset to their educational journey. Overall, we identified 2 overarching themes (1) Humanistic aspects of pharmacy learner care; and (2) Intrinsic aspects of pharmacy learner care. There was marked divergence when comparing and contrasting patients' and pharmacy learners' data. Subthemes further revealed that humanistic aspects include rapport, simple language, and active listening as pharmacy learner skills patients felt they could assess. Conversely, pharmacy learners expected patients to predominantly assess their intrinsic pharmacy skills including knowledge and optimization of health. CONCLUSION: This study provides insight into how real patients could participate in the assessment of pharmacy learners and how this participation was perceived by learners themselves. We encourage pharmacy educators to incorporate patient perspectives into the content/curricula of their training programs as an inclusive approach to learner assessment. We also recommend developing a patient feedback tool informed by our study findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".