An Analysis of Canadian Doctor of Pharmacy Student Experiences in Non-Traditional Student-Preceptor Models
Bibliographic record
Abstract
Objective. To describe students’ experiences and perceptions of non-traditional student-preceptor learning models and evaluate the effectiveness of these models on students’ learning experience. Methods. Pharmacy students who had completed at least one experiential rotation with a non-traditional learning model participated in semi-structured interviews. Models included peer-assisted learning (PAL; two or more students of same educational level), near-peer teaching (NPT; one or more junior students with one or more senior students), and co-preceptorship (CoP; two or more preceptors). Interviews were transcribed, coded, and analyzed for themes. Themes were mapped according to the Kirkpatrick model for evaluating educational training. Results. Twenty semi-structured interviews were conducted. Forty-three experiences (19 CoP, 14 PAL, 10 NPT) from 14 institutions were described. Many themes overlapped between the three models. In CoP, learners described increased preceptor availability and exposure to different patient care approaches. Challenges arose when preceptors had different expectations. Students overwhelmingly endorsed a multi-learner environment. Both PAL and NPT learners felt supported as collaboration with other learners was readily fostered. Potential challenges in PAL and NPT were difficulties when personalities conflicted and when there was a significant knowledge gap between the learners. All three models allowed for the development of skills, including communication and collaboration. Learners reported an enhanced approach to patient care and professional practice, including approaches to teaching as new preceptors. Conclusion. Pharmacy students and graduates valued their experiences in non-traditional student-preceptor models. Institutions may find support for using these precepting models to increase placement capacity.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".