Opioids prescribed by pharmacist under the Health Canada’s Controlled Drugs and Substances Act temporary exemption
Bibliographic record
Abstract
BACKGROUND: During the coronavirus disease 2019 (COVID-19) pandemic, Health Canada issued a temporary exemption for the Controlled Drugs and Substances Act (CDSA). Very little is known about pharmacists prescribing opioids under the CDSA temporary exemption. OBJECTIVE: This study aimed to evaluate the impact of CDSA subsection 56(1) temporary exemption on prescribing of opioids by direct patient care pharmacists during COVID-19 between February 1, 2018, and April 30, 2022. METHODS: Descriptive statistics (sample mean, sample SD, sample proportion) and data visualization tools were used to explore the possible changes owing to CDSA. In the first stage, a linear regression model was fit to the data to detect the changes. Second, the time dependence of the data was checked by examining the autocorrelation plots and testing the dependence of the residuals, and then a suitable time series process was used. RESULTS: The mean overall pharmacist-prescribed opioid weekly claims increased from 0.0 (per-CDSA policy period) to 57.0 (post-CDSA policy period). The time series regression for the mean-level change for the overall prescription data was 36.29 (95% CI 27.14-48.52, P < 0.0001). The time series regression for the mean-level change for the analgesic prescription data and the opioid use disorder prescription data was 28.95 (95% CI 20.88-40.13, P < 0.0001) and 6.74 (95% CI 5.80-7.82, P < 0.0001). CONCLUSIONS: The temporary exemption under the CDSA during the COVID-19 pandemic allowed pharmacists in Nova Scotia to prescribe opioids, ensuring continuity of opioid therapy for adults. Future studies are needed to investigate the reasons behind the low uptake of CDSA exemptions by pharmacists involved in direct patient care.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".