Lessons Learned: A Pilot Program to Cultivate a Clinical Role for Pharmacy Students
Bibliographic record
Abstract
INTRODUCTION For many years, first-, second-, and third-year pharmacy students were hired into technical roles for summer employment within the pharmacy department at Capital Health in Edmonton, Alberta. A pilot program to cultivate a clinical role for summer pharmacy students as members of the pharmacy patient care team was undertaken in 2004 by the clinical coordinators (including MLA) at Capital Health. This pilot program was designed to develop a rewarding professional development experience for pharmacy students, to offer meaningful contributions to the pharmacy clinical teams, to positively affect pharmacy students’ views of hospital practice, and to encourage careers in hospital pharmacy. In addition, integrating patient care and professional activities into student positions held promise to initiate change within the department’s culture with respect to students. The role developed for the pilot program was based on clinical pharmacy technician models from the literature, in which the clinical technician provided clinical services support, such as collecting laboratory data, screening patients, taking medication histories, and tracking outcomes, so as to redirect clerical workload and focus pharmacists’ time on direct patient care activities.1-4 These models involved extensive training for specific roles according to established protocols and other clinical tools, as well as assessment of decisionmaking competence for screening tasks.2,4 These previously reported experiences inspired development of a targeted clinical training program at our own institution. In addition, it was felt that a clearly defined role and expectations, supported by targeted training in clinical patient care activities, would promote acceptance of students on the pharmacy clinical team. Capital Health is a regional health authority serving a population of approximately one million. It consists of 13 facilities with a total of more than 2500 beds, in addition to outpatient clinics and public health services. Capital Health provides academic training for the health care professional programs of the University of Alberta. Regional Pharmacy Services, through its nearly 100 pharmacists and more than 65 technicians, provides clinical and distribution services for the health region. In this report we describe our experiences with this pilot program and outline the lessons learned.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".