Knowledge, perceptions, and attitudes toward pharmacogenomics among pharmacists and pharmacy students: A systematic review
Bibliographic record
Abstract
Background and Aims: Pharmacists have been recognized as one of the most qualified healthcare professionals in the clinical implementation of pharmacogenomics, yet its widespread implementation in clinical pharmacy practice has remained limited. The review aims to systematically investigate knowledge, perceptions, and attitudes toward pharmacogenomics among pharmacists and pharmacy students to inform the future delivery of pharmacogenomics education programs. Methods: PubMed, MEDLINE, Embase, Scopus, and the International Pharmaceutical Abstracts were searched up to May 17, 2022. Studies were selected if they included data on pharmacists' or pharmacy students' knowledge, perception, or attitude about pharmacogenomics and were published in a peer-reviewed, English-language journal with full-text availability. Any published study not deemed original research was excluded. All included studies were critically appraised using the Center for Evidence-Based Management's critical appraisal tools. The data were descriptively analyzed and presented based on pharmacists' and pharmacy students' knowledge/awareness, perception/attitudes toward pharmacogenomic (PGx), confidence in using or interpreting PGx testing results, and their desire to get further PGx education or their most preferred method of further education. Results: A combined total of 12,430 pharmacists and pharmacy students from 26 countries are represented in the 52 included studies. Despite overwhelmingly positive attitudes and perceptions toward pharmacogenomics among pharmacists and pharmacy students, an overall lack of adequate knowledge and confidence was found. The review also found a strong desire for further pharmacogenomics education among pharmacists and pharmacy students. Conclusion: Pharmacists and pharmacy students have positive perceptions and attitudes toward pharmacogenomics, which is hindered by a lack of knowledge and confidence. However, inadequate control for confounders, limited representativeness of the studied population or region, and small sample sizes diminish the generalizability of the review results. Knowledge and confidence could be improved through enhanced delivery of pharmacogenomic courses within the pharmacy curriculum and continuing education programs.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".