Experiential Training for Pharmacy Students: Time for a New Approach
Bibliographic record
Abstract
The pharmacy profession in Canada finds itself in the midst of transformational change. The regulation and expanded scope of practice of pharmacy technicians that is under way across the country, combined with the advent of new drug distribution technologies in both the hospital and community practice settings, have created the opportunity for pharmacists to largely relinquish their traditional role in the drug distribu tion system. This change will permit pharmacists to take on new and expanded patient-centred roles and responsibilities. However, patient-centred care requires a different set of skills and training (e.g., clinical practice skills, critical thinking skills, decision-making skills under conditions of uncertainty, collab orative interpersonal practice skills) than those that were needed to be a good pharmacist in the past (e.g., attention to detail, accuracy, risk aversion). Pharmacy educational programs will have to produce confident graduates who are able and willing to assume responsibility and accountability for drug therapy management. Experiential training is viewed as an essential requirement for the development of these skills. As the profession moves forward with implementation of the Vision for Pharmacy contained within the Blueprint for Pharmacy, 1 there has been
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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 | 0.037 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.007 | 0.031 |
| Research integrity | 0.015 | 0.027 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".