Pharmacy Student Perceptions of Pharmacist Prescribing: A Comparison Study
Bibliographic record
Abstract
Several jurisdictions throughout the world, such as the UK and Canada, now have independent prescribing by pharmacists. In some areas of Canada, initial access prescribing can be done by pharmacists. In contrast, Australian pharmacists have no ability to prescribe either in a supplementary or independent model. Considerable research has been completed regarding attitudes towards pharmacist prescribing from the perspective of health care professionals, however currently no literature exists regarding pharmacy student views on prescribing. The primary objective of this study is to examine pharmacy student’s opinions and attitudes towards pharmacist prescribing in two different settings. Focus groups were conducted with selected students from two universities (one in Canada and one in Australia). Content analysis was conducted. Four main themes were identified: benefits, fears, needs and pharmacist roles. Students from the Australian University were more accepting of the role of supplementary prescribing. In contrast, the Canadian students felt that independent prescribing was moving the profession in the right direction. There were a number of similarities with the two groups with regards to benefits and fears. Although the two cohorts differed in terms of their beliefs on many aspects of prescribing, there were similarities in terms of fears of physician backlash and blurring of professional roles.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".