Differences and over‐time changes in levels of prescription opioid analgesic dispensing from retail pharmacies in Canada, 2005–2010
Bibliographic record
Abstract
PURPOSE: To examine qualitative and quantitative levels and trends of prescription opioid analgesics ("opioids") use and the potential impact of prescription monitoring programs (PMPs), in the 10 Canadian provinces, for 2005-2010. METHODS: Opioid dispensing data from a representative sample of 2700 retail pharmacies were obtained. Individual opioid dispensing values were translated into defined daily doses per day/1000 population and categorized into "weak opioids" and "strong opioids" by standardized methods. Opioid prescription rates between provinces and over time, as well as the impact of PMPs, were examined using regression analyses techniques (i.e., Poisson, ANOVAs). RESULTS: Significant differences between provinces in the overall standardized rates of dispensing for total opioids, as well as for "weak opioids" and "strong opioids" categories, were found. The majority of provinces featured increases or curvilinear trends in the standardized amounts of opioids dispensed over time, mainly driven by increases in "strong opioids" use. In addition, significant inter-provincial differences in the levels of dispensing of individual opioids were found. Comparisons of changes in opioid dispensing between provinces with and without PMPs did not indicate significant differences. CONCLUSIONS: Opioid use featured significant quantitative and qualitative differences between provinces in Canada and showed an overall increasing trend mainly driven by changes in "strong opioids" in the study period. Reasons for the observed differences are not clear yet require systematic examination to allow evidence-based interventions in the interest of equitable pain treatment as well as the reduction of high levels of opioid-related morbidity and mortality in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".