Evaluation of a New Hierarchical Teaching Model for Pharmacy Students in Experiential Education
Bibliographic record
Abstract
Pharmacy students undergoing experiential education have traditionally been mentored by pharmacist preceptors on a one-to-one basis. However, major curriculum changes are placing greater emphasis on experiential education. For example, the new PharmD curriculum within the University of Toronto’s Leslie Dan Faculty of Pharmacy requires Early Practice Experience (EPE) rotations of 160 h each after year 1 and year 2 (in place of the 12-h Early Hospital Experience [EHE] in year 2 of the previous program) and 36 weeks of experiential practice after year 3. Implementation of these changes has begun only within the past year (i.e., since mid-2012), and it will take some time to realize the full magnitude of the effect on staff workload at clinical sites; however, rotation requirements have already increased dramatically. In general, teaching sites strive to offer placements that are of high quality while accommodating the needs of pharmacist preceptors and clinical programs. This large increase in pharmacy student rotations has created a stress on the system and has forced sites to explore alternative methods of delivering experiential education. The hierarchical model of teaching, with senior students mentoring junior students, is well known in the medical field. However, little is known about how effective or feasible this model might be for pharmacy students. Lindblad and others described peer-assisted learning for final-year pharmacy students on a 9-week rotation in a general medicine/stroke care unit. In that model, the first group of 3 students started their rotation at week 1, with the second group of 3 students starting at week 5. This allowed for overlap and mentoring by the more experienced students during weeks 5 to 9. The second group then continued their rotation until week 13. However, such peer-assisted learning is based on more experienced students facilitating the learning of a group of peers who are all at the same point in their education. It may not be suitable for models in which students are at different points in their academic programs. Given the prospect of pharmacy students from different years completing rotations simultaneously, a hierarchical model may be one way to accommodate the increased demand for placements and to allow pharmacists to dedicate time to teaching in addition to their dispensing, clinical, and research duties. As described by Hall and others, pharmacy educators at Henry Ford Hospital in Detroit, Michigan, hypothesized that a hierarchical system similar to the medical model could be successful. Under this structure, pharmacy students at various levels (e.g., students, residents, fellows) would be under the supervision of an “attending” practitioner, who would be most responsible for the quality of patient care even as the students progressively assumed greater independence and more responsibilities. However, the changing availability of students from month to month was recognized as a potential barrier to full implementation of the system. Here, we report an evaluation of a hierarchical teaching model in which fourth-year pharmacy students and residents mentored junior pharmacy students under the supervision of a pharmacist, in terms of perceived impact on learning, overall experience, and pharmacists’ workload.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".