Trends in the dispensing of opioids for pain and concurrent benzodiazepine use among First Nations People in Ontario, Canada, from 2013 to 2021
Bibliographic record
Abstract
OBJECTIVES: To investigate dispensing trends and the characteristics of First Nations People in Ontario dispensed an opioid for pain and concurrent benzodiazepine treatment. METHODS: We conducted a population-based serial cross-sectional study by quarter of registered (Status) First Nations People in Ontario who were dispensed an opioid for pain between April 1, 2013, and December 31, 2021. We reported quarterly trends in prevalent and incident opioid dispensing (rates per 1000 people), and the prevalence of concurrent benzodiazepine use among individuals receiving opioids for pain. For the final year (2021), we stratified rates by age, place of residence (within or outside First Nations communities), and sex. RESULTS: Between 2013 and 2021, the quarterly rate of opioid dispensing for pain decreased by 25.0% among First Nations People in Ontario, from 74.7 to 56.0 per 1000 people. In stratified analyses for the year 2021, opioid use for pain was more frequent among First Nations People living outside versus within First Nations communities (118.2 vs. 91.2 per 1000, respectively) and among females relative to males (124.6 and 93.9 per 1000, respectively). Concurrent prescription benzodiazepine use among First Nations People receiving a prescription opioid for pain decreased from 20.9% in Q2 2013 to 16.7% in Q4 2021. In stratified analyses, concurrent use was more prevalent among females, adults aged ≥ 65 years, and First Nations People living outside First Nations communities. CONCLUSION: Opioid analgesic prescribing patterns for First Nations People living in Ontario indicate a decrease in both overall prescribing rates and concurrent benzodiazepine use.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".