Pharmacists as Personalized Medicine Experts (PRIME): Experiences Implementing Pharmacist-Led Pharmacogenomic Testing in Primary Care Practices
Bibliographic record
Abstract
Research exploring the integration of pharmacogenomics (PGx) testing by pharmacists into their primary care practices (including community pharmacies) has focused on the "external" factors that impact practice implementation. In this study, additional "internal" factors, related to the capabilities, opportunities, and motivations of pharmacists that influence their ability to implement PGx testing, were analyzed. Semi-structured interview data from the Pharmacists as Personalized Medicine Experts (PRIME) study, which examined the barriers and facilitators to implementing PGx testing by pharmacists into primary care practice, were analyzed. Through thematic analysis, using the theoretical domains framework (TDF) domains as deductive codes, the authors identified the most relevant TDF domains and applied the behavioural change wheel (BCW) to generate intervention types to aid in the implementation of PGx testing. Pharmacists described how their professional identities, practice environments, self-confidence, and beliefs in the benefits of PGx impacted their ability to provide a PGx-testing service. Potential interventions to improve the implementation of the PGx service included preparing pharmacists for managing an increased patient load, helping pharmacists navigate the software and technology requirements associated with the PGx service, and streamlining workflows and documentation requirements. As interest in the wide-scale implementation of PGx testing through community pharmacies grows, additional strategies need to address the "internal" factors that influence the ability of pharmacists to integrate testing into their practices.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".