Assessing pharmacists’ knowledge and compliance with narcotic inventory management using a computer-based educational platform
Bibliographic record
Abstract
OBJECTIVES: Pharmacy professionals are required to take all necessary steps to protect commonly misused drugs such as opioids at their pharmacies to minimize the risk of diversion. The aim of this study is to assess Canadian pharmacy professionals' knowledge and compliance with federal and provincial regulations using the computer-based educational platform Pharmacy5in5. METHODS: A Narcotic Inventory module was created and reviewed by experts representing provincial and federal regulators. Descriptive statistics were used to analyze users' performance in quizzes. Binomial regression and logistic regression models were used to investigate the effect of demographic factors on users' performance. P-values less than 0.05 were considered statistically significant. KEY FINDINGS: The analysis included data collected over a period of three months. A total of 792 users accessed the Narcotic Inventory module on the Pharmacy5in5 website between July 2019 and November 2019. Most of the users were licenced pharmacists (64%), female (72%), received their training in Canada (68%), and were practising in Ontario (80%). Users performed best on the quiz addressing the steps for reconciliation of inventory (93%), and worst on the quiz reviewing how to prepare for a Health Canada visit (66%). CONCLUSIONS: Overall, pharmacy professionals showed adequate knowledge of the CDSA and provincial/territorial regulations regarding opioids inventory management. Conversely, the study highlighted poor compliance with the reporting of losses and theft of controlled substances by pharmacy professionals. Innovative approaches are needed to influence pharmacy professionals' behaviours to improve their compliance with best practices concerning inventory management to reduce drug diversion.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".