Ontario pharmacists’ perceptions of the Pharmaceutical Opinion Program
Bibliographic record
Abstract
In 2011, the Ontario government introduced the Pharmaceutical Opinion Program (POP),1 through which pharmacists are compensated $15 for identifying a potential drug-related problem. This can occur during the course of dispensing a new or repeat prescription or when conducting a MedsCheck medication review. The pharmacist is expected to provide a clinical recommendation and rationale to the prescriber to address the problem. The end outcome of the consultation could be to dispense the prescription originally prescribed, to change the prescription (dose, duration, product, etc.) or to refuse to fill the prescription. The POP is currently only available to recipients of the Ontario Public Drug Programs. Since the program was introduced in April 2011, pharmacists have provided more than 700,000 pharmaceutical opinions, the majority (68%) of which resulted in a changed prescription (Allan Malek, Ontario Pharmacists Association, personal communication, June 20, 2013). Anecdotal reports suggest that pharmacist uptake of this program has been suboptimal, in part due to the misunderstanding of program requirements. Yet there has been no research to date about pharmacists’ experiences with and perceptions of the program. The goal of this research project was to ascertain the barriers and facilitators that community pharmacists have experienced while providing pharmaceutical opinions to their patients.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".