Evaluating the potential for pharmacists to prescribe oral anticoagulants for atrial fibrillation
Bibliographic record
Abstract
Background: Oral anticoagulant therapy (OAC) to prevent atrial fibrillation (AF)–related strokes remains poorly used. Alternate strategies, such as community pharmacist prescribing of OAC, should be explored. Methods: Approximately 400 pharmacists, half with additional prescribing authority (APA), randomly selected from the Alberta College of Pharmacists, were invited to participate in an online survey over a 6-week period. The survey consisted of demographics, case scenarios assessing appropriateness of OAC (based on the 2014 Canadian Cardiovascular Society AF guidelines) and perceived barriers to prescribing. Regression analysis was performed to determine predictors of knowledge. Results: A total of 35% (139/397) of pharmacists responded to the survey, and 57% of these had APA. Depending on the case scenario, 55% to 92% of pharmacists correctly identified patients eligible for stroke prevention therapy, but only about a half selected the appropriate antithrombotic agent; there was no difference in the knowledge according to APA status. In multivariable analysis, predictors significantly associated with guideline-concordant prescribing were having the pharmacist interact as part of an interprofessional team ( p = 0.04) and direct OAC (DOAC) self-efficacy (confidence in ability to extend, adapt, initiate or alter prescriptions; p = 0.02). Barriers to prescribing OAC for APA pharmacists included a lack of AF and DOAC knowledge and preference for consulting the physician first, but these same pharmacists also identified difficulty in contacting the physician as a major barrier. Interpretation and Conclusion: Community pharmacists can identify patients who would benefit from stroke prevention therapy in AF. However, physician collaboration and further training on AF and guidelines for prescribing OAC are needed.
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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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".