Bibliographic record
Abstract
Six Canadian provinces allow pharmacists to prescribe for ambulatory conditions (also sometimes referred to as “minor ailments”; see below). In 2007, Alberta became the first province to lay the legislative groundwork for a pharmacist-led ambulatory condition program (PACP) through its “Additional Prescribing Authority.”1,2 In 2011, Nova Scotia and Saskatchewan introduced their PACPs. In 2014, Manitoba, New Brunswick and Prince Edward Island followed suit.2–4 Most recently, British Columbia and Newfoundland have submitted proposals for PACPs.5,6 In Ontario, despite advocacy efforts by the Ontario Pharmacists Association (OPA) and support from the Ontario College of Pharmacists (OCP), prescribing for ambulatory conditions was not part of the 2012 scope of practice changes.3,7 The Health Professions Regulatory Advisory Council (HPRAC), responsible for the scope of practice changes, reported that pharmacists had the necessary training, but suggested a working group be formed to discuss possible frameworks.7 To date, this group has not been convened. This commentary highlights 5 controversies regarding Canadian pharmacist-led ambulatory conditions programs, all of which are important considerations for other jurisdictions moving forward with such programs.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".