Contemporary Professional Skills Development for Pharmacists in the Middle East
Bibliographic record
Abstract
Objective. To determine professional skills development and its utility among the “bridge” curriculum for undergraduate and graduate students in the Middle East. Methods. Qatar University College of Pharmacy offers a part-time Doctor of Pharmacy (PharmD) program for licensed pharmacists, which includes pre-internship or “bridge” courses adapted from the undergraduate baccalaureate program. Assessments for all professional skills courses delivered in the undergraduate and post-baccalaureate part-time PharmD curriculums between 2011 and 2015 academic years were inventoried. The number and nature of assignments and exams administered to both student cohorts were identified and aggregate class scores recorded. Results were compared using Mann-Whitney tests for non-parametric continuous data with significance level (2-sided) set at α <.05. Results. Twenty-seven common assessments were conducted over a 5-year period. Overall, the performance between the undergraduate and graduate students was comparable except for specific assignments and in certain cohorts. Chart note documentation skills were poor among part-time PharmD students in both professional skills years and may be attributed to lack of prior instruction or current use in practice. Conclusion. Our comparison of graduate and undergraduate student performance in a professional skills course series has reinforced its legitimacy in our part-time PharmD bridge curriculum. Such quality assurance is relevant for programs offering advanced degree training for licensed professionals to ensure ongoing alignment of student abilities with desired educational outcomes and ultimately, delivery of patient care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".