Will I lose my license for that? A closer look at Canadian disciplinary hearings and what it means for pharmacists’ practice to full scope
Bibliographic record
Abstract
OBJECTIVE: Concerns about liability from clinical errors have been cited as a barrier preventing greater adoption of practice change. Our objective was to determine the most common actions or omissions that result in disciplinary action for pharmacists and the restrictive actions imposed. METHODS: Canadian disciplinary reports were reviewed. Cases were coded as charges of professional misconduct, unskilled practice or dishonest business practices. RESULTS: There were 558 disciplinary cases from 10 provinces that occurred between January 2010 and July 2017. Professional misconduct charges commonly involved stealing/diverting or inappropriately dispensing narcotic drugs, pharmacy supervision/premises charges and refusing to cooperate with the college. Charges of unskilled practice included dispensing the wrong drug, failing to assess the appropriateness of a drug order, providing the wrong dose and failing to counsel. Fraudulent billing practices and accepting rebates from generic drug companies were the most common dishonest business practices. Professional misconduct, unskilled practice and dishonest business practice charges were involved in 342 (61%), 169 (30%) and 191 (34%) cases, respectively. Most cases occurred in community pharmacies and were not caused by an isolated clinical error. Fines were the most common penalty, followed by temporary license suspensions, professional development and reprimands. License revocations were the least common (4%), often involving professional misconduct. CONCLUSION: This review suggests that disciplinary action against a pharmacist for an isolated, unintentional clinical error is uncommon and that losing a license is rare. Fear of disciplinary action should not be a barrier to practice change or the provision of full-scope patient care services.
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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 | 0.011 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".