Pharmacist prescribing training models in the United Kingdom, Australia, and Canada: Snapshot survey
Bibliographic record
Abstract
Background: This study sought to identify the differences between training models for pharmacist prescribing across three countries according to the funding, model of prescribing the pharmacist will practice after training, training course framework, method of delivery, assessment, continuing professional development, and barriers and facilitators to enrolment. Methods: An online quantitative/qualitative snapshot survey was sent to academics of pharmacist prescribing courses and Deans of different pharmacy schools in the UK (n=49), Australia (n=12), and Canada (n=10). A narrative analysis was undertaken. Results: Seventeen pharmacy schools responded (24% response rate). The UK provides postgraduate training courses funded by the government. Canada incorporates prescribing competencies into entry-t- practice courses. Australia does not provide courses yet. Conclusion: Pharmacist prescribing is still under-utilised in many countries. Standardisation would reduce variation and improve uptake in countries implementing pharmacist prescribing roles. However, there is currently no international unified system or curriculum for pharmacists' prescribing courses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".