Ontario Pharmacists' Crisis over Bill 16: A Missed Opportunity?
Bibliographic record
Abstract
INTRODUCTION: In 2010, the Ontario government brought forward Bill 16, which, among other things, removed pharmacists' professional allowances. While many would disagree with this unilateral action by the Ontario government, it also could have served as a crisis for change towards patient-centred care. We sought to examine the response of the pharmacy profession in Ontario to this crisis as it relates to the vision outlined in the Blueprint for Pharmacy. METHODS: We systematically examined publicly available responses to Schedule 5 of Ontario's Bill 16 during the period from April to June 16, 2010. A rapid textual analysis of the data using tag or word clouds and a qualitative content analysis were performed on all of the data collected. RESULTS: The rapid textual analysis revealed that the most frequently used terms were "pharmacist," "pharmacy" and "professional allowances"; the least used were "layoffs," "service cuts" and "patient care." Content analysis revealed 4 themes: the desire to maintain the status quo of practice, a focus on the business of pharmacy, pharmacy stakeholders' perceptions of government's attitude towards the profession and changes to patient services. DISCUSSION: It is notable that patient care was almost completely absent from the discussion, a reflection that our profession has not embraced patient-centred care. This also represents a missed opportunity - a crisis that could have been used to move the profession towards the Blueprint's vision. We thought that the Blueprint had already achieved this consensus, but the Ontario experience has shown that this may not be the case.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".