Grading pharmacists’ risk of complaints to a regulator: A retrospective cohort study
Bibliographic record
Abstract
Background: Tools to grade risk of complaint to a regulatory board have been developed for physicians but not for other health practitioner groups, including pharmacists. We aimed to develop a score that classified pharmacists into low, medium and high risk categories. Methods: Registration and complaint data were sourced from Ontario College of Pharmacists for January 2009 to December 2019. We undertook recurrent event survival analysis to predict lodgement of a complaint. We identified those variables that were associated with a complaint and included these in a risk score which we called PRONE-Pharm (Predicted Risk of New Event for Pharmacists). We assessed diagnostic accuracy and used this to identify thresholds that defined low, medium and high risk. Results: We identified 3,675 complaints against 17,308 pharmacists. Being male (HR = 1.72), older age (HR range 1.43–1.54), trained internationally (HR = 1.62), ≥1 prior complaint (HR range 2.83–9.60), and complaints about mental health or substance use (HR = 1.91), compliance with conditions (HR = 1.86), fees and servicing (HR = 1.74), interpersonal behaviour or honesty (HR = 1.40), procedures (HR = 1.75) and treatment or communication or other clinical issues (HR = 1.22) were all associated with lodgement of a complaint. When converted into the PRONE-Pharm risk score, pharmacists were assigned between 0 and 98 points with higher scores closely associated with higher probability of a complaint. A score of ≥25 had sufficient accuracy for classifying medium-risk pharmacists (specificity = 87.0%) and ≥45 for high-risk pharmacists (specificity = 98.4%). Conclusion: Distinguishing isolated incidents from persistent problems poses a significant challenge for entities responsible for the regulation of pharmacists and other health practitioners. The diagnostic properties of PRONE-Pharm (minimizing the false positives) means that the risk score is useful for “ruling-out” low risk pharmacists using routinely collected regulatory data. PRONE-Pharm may be useful when used alongside interventions appropriately matched to a pharmacist’s level of risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".