Prescription drug abuse in Canada and the diversion of prescription drugs into the illicit drug market
Bibliographic record
Abstract
Prescription drug abuse has received considerable attention in media reports in recent years. The purpose of this article is to describe the Canadian situation and context with regards to prescription drug abuse and the diversion of psychotropic prescription drugs into the illicit drug market, with a focus on the need for more data and interventions. Canada ranks within the top 10% of countries in the use of benzodiazepines, opioid prescriptions and stimulants. There are many ways that prescription drugs are diverted into the illicit market and varied reasons for use and abuse. Prescription drug abuse is further related to a number of negative consequences, including overdose. While seniors and women have been the primary focus for research in Canada on prescription drug abuse, adolescents and young adults have received less attention. Systematic epidemiological data specifically on prescription drug abuse in the Canada context are lacking and are needed in order to more clearly understand the reasons for the phenomenon and to develop and implement appropriate interventions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".