Use and Nonmedical Use of Prescription Opioid Analgesics in the General Population of Canada and Correlations with Dispensing Levels in 2009
Bibliographic record
Abstract
BACKGROUND: In Canada, harm from nonmedical prescription opioid analgesic (POA) use (NMPOU) has increased in recent years; however, there are limitations to the current estimates of NMPOU. The 2009 Canadian Alcohol and Drug Use Monitoring Survey presents an opportunity to produce more accurate estimates of NMPOU. OBJECTIVES: To determine the prevalence of POA use, NMPOU and use of pain relievers to 'get high', and to assess correlations of these indicators with age, sex and provincial levels of dispensed POAs in Canada in 2009. METHODS: Data regarding POA use were obtained from the 2009 Canadian Alcohol and Drug Use Monitoring Survey (n=13,032). The amount of POAs dispensed in standardized daily doses was obtained from a representative sample of 2700 retail pharmacies across Canada. Associations among POA use, age, sex and the amount of POAs dispensed were evaluated using regression models. Differences in POA use across provinces were assessed using the Wald test. RESULTS: In Canada in 2009, the prevalence of POA use was 19.2% (95% CI 18.0% to 20.5%), NMPOU was 4.8% (95% CI 4.1% to 5.5%) and the use of pain relievers to get high was 0.4% (95% CI 0.1% to 0.8%). NMPOU was significantly associated with age. The use of pain relievers to get high varied significantly across provinces, while POA use and NMPOU did not show significant variations. The amount of POAs dispensed per province was not significantly correlated with any type of POA use. CONCLUSIONS: These findings confirm high POA use and NMPOU across Canada. Research is required to identify determinants of NMPOU.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".