Incorporating assessment and prescribing for ambulatory ailments skills into practice: An environmental scan of continuing education for pharmacist prescribing in Canada
Bibliographic record
Abstract
OBJECTIVES: Pharmacists in Canadian provinces are at different stages of applying prescribing legislation into practice. The purpose of this environmental scan was to examine differences in legislation, remuneration, professional uptake, continuing education requirements and continuing education resources relating to pharmacist prescribing for ambulatory ailments, with a focus on continuing education. METHODS: Data were collected between May and December 2016 using websites and communication with provincial professional regulatory bodies, advocacy bodies, drug coverage programs and other organizations that offer continuing education for pharmacists. RESULTS: Training requirements to prescribe for ambulatory ailments vary provincially, including no training requirements, online tutorials and a comprehensive application process. Government-funded remuneration for prescribing services is absent in most provinces. Pharmacist uptake of the training required to obtain prescribing authority ranges from 30% to 100% of pharmacists. Continuing education programs on the topic of prescribing across the country include online courses, in-person courses, webinars, panel discussions and preparation courses. CONCLUSION: Many aspects of pharmacist prescribing for ambulatory ailments, including the style and content of continuing education resources, vary from province to province. Further research on this topic would help to determine the effect of these differences on the success of incorporating pharmacist prescribing into practice.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".