Exploring Pharmacists' Understanding and Adoption of Prescribing in 2 Canadian Jurisdictions: Design and Rationale for a Mixed-Methods Approach
Bibliographic record
Abstract
Background: Although pharmacists have been granted prescribing privileges in Alberta since 2007 and most other Canadian provinces have granted or put forward similar legislation since then, there is a lack of Canadian data exploring pharmacists' understanding and adoption of prescribing practice. Therefore, our goal is to investigate pharmacists' perceptions of prescribing, the extent to which prescribing has been incorporated into pharmacists' practices and the factors that have influenced its uptake. Methods (Study Design): We are conducting a mixed-methods evaluation of pharmacist prescribing in Alberta and Ontario consisting of 3 consecutive stages: 1) semi-structured interviews with a small cohort of pharmacists in Alberta and Ontario; 2) development of a survey guided by responses identified in Stage 1; and 3) a mixed-methods survey of a large random sample of pharmacists in Alberta. Conclusion: When complete, this study will inform researchers, policy-makers and educators about Ontario pharmacists' attitudes towards prescribing and the initial uptake of prescribing practice in Alberta. This will allow for a greater understanding of pharmacists' perceptions of prescribing; the extent to which prescribing has been incorporated into pharmacy practice; and the factors facilitating its uptake.
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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 | 0.065 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".