The Efficacy of a Didactic and Case-Based Pharmacogenomics Education Program on Improving the Knowledge and Confidence of Alberta Pharmacists
Bibliographic record
Abstract
Background: Pharmacogenomics (PGx) is the study of how genetic variations for functional proteins, such as metabolizing enzymes and drug receptors, impact drug pharmacokinetics and pharmacodynamics. In theory, pharmacists are well suited to utilize PGx in tailoring medications to patient genetics when providing medication therapy management services. However, PGx education needs to reach pharmacists prior to implementation. The aim of this study is to develop and evaluate a PGx course for pharmacists. Methods: A PGx education program was created and offered synchronously (virtual) and asynchronously (self-study) to pharmacists in Alberta, Canada. Lectures were delivered by experts live (virtual) with a question-and-answer period for synchronous sessions. These sessions were recorded for asynchronous delivery. Six case studies were discussed in large and small groups ("breakout rooms") in synchronous sessions, and provided for self-study in the asynchronous subgroup. Topics included genetic and PGx concepts; therapeutic applications; ethical, legal, and social considerations; and practical implementation. Pre- and post-course surveys measured self-rated knowledge using a 5-point Likert Scale and tested objective knowledge with a graded quiz. Results: Thirty-six pharmacists completed the course and both surveys. Participants reported backgrounds in community (88.9%) and hospital (38.9%) practice. Prior education in PGx was reported by 44.4% from degree programs and 27.8% from continuing education. Overall responses to statements about confidence in PGx moved from a median of "Disagree" at baseline to "Agree" after receiving PGx education (2-point difference [1,2] on 5-point Likert Scale; p < 0.001), indicating an increase in self-assessed competency in PGx. Likewise, mean participant grades on the knowledge quiz improved (20.8±21.9% pre-course vs 70.2±19.1% post-course, p < 0.001). There was no difference in these results between synchronous and asynchronous groups. Conclusion: A didactic and case-based PGx education program was effective at increasing pharmacist knowledge and confidence in PGx in both synchronous and asynchronous environments. Knowledge gained can be utilized in delivery of patient-centered, personalized medication therapy management in the pharmacy setting.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".