Graduating a More Confident Pharmacist: Applying the Medical Model of Training
Bibliographic record
Abstract
Ihave a dream — that some day clinical training for pharmacy undergraduate students will parallel that of medical students. In my own undergraduate years, I never saw a patient — or another health care professional, for that matter. Fortunately, pharmacy undergraduate training has progressed since then, but the process has been long and slow, beginning with visits to clinical sites and observation of pharmacists in their roles, an experience in which no responsibilities were delegated to the student. Now, each student completes clinical clerkships of several weeks to months, during which they are integrated into the management teams caring for patients in hospitals, clinics, and community pharmacies. Unfortunately, however, such opportunities are generally offered at the very end of undergraduate training. As such, the student goes directly from primarily a setting of large classes and few expectations (except for the occasional problem-based learning discussion), to a setting of patient assignments and interactions, as well as small-group discussions within the health care team, with expectations of acquiring and discussing new knowledge, and applying it to assigned patients under close supervision. No wonder these pharmacy students appear to have, and sometimes admit to, an insufficient degree of confidence in providing any level of pharmaceutical care. Yet these students are only weeks to months away from graduating and becoming licensed pharmacists. My 30 years of participating in clinical education for pharmacy and medical trainees has given me a unique view of how our respective professional faculties teach and, more important, train their prospective graduates. Medical training introduces students to the responsibility of taking care of patients in the first undergraduate year. This experiential approach progresses, such that thirdand fourth-year medical students spend all of their time in the clinical setting, primarily in teaching hospitals and academic family practice units, accepting increasing responsibility and acquiring practical clinical knowledge. This undergraduate education culminates in, at a minimum, a 2-year postgraduate residency for family practitioners and several additional years for general internists and specialists. The General Medical Council of Great Britain has recently recommended increased and earlier patient contact for medical students in that country. 1
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".