Monitoring Programs for Drugs with Potential for Abuse or Misuse in Canada
Bibliographic record
Abstract
The compliance and monitoring of controlled substances in Canada are regulated by Health Canada's Controlled Drugs and Substances Act. The Controlled Drugs and Substances Act (CDSA) was passed in 1996 to replace The Narcotic Control Act and Food and Drugs Act, Parts III and IV. It establishes 8 schedules of controlled substances and 2 classes of precursors. While the importation, production, distribution and possession of various drugs and substances in Canada are governed primarily by the provisions of the CDSA, it does not regulate or monitor the prescribing of narcotics or controlled substances. At present in Canada, there are no national monitoring or comprehensive surveillance systems in place to identify, monitor, record and track the diversion, abuse and misuse of narcotics or controlled substances. As a result, individual provinces have established prescription monitoring programs to promote the appropriate use of certain monitored drugs with the potential for abuse, misuse and diversion for nonmedical purposes. Currently, in Canada, there are 7 monitoring programs, with 1 program in development. The goal is to reduce the abuse or misuse of monitored drugs in these provinces. The objective of this paper is to provide a summary of the drug monitoring programs available in Canada, the review process to include or exclude drugs in the program and program evaluation for outcomes.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".