Population-based prescription opioid use rate in Newfoundland and Labrador: A retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To report the rate of prescription opioid use rates over a 5-year period for the population of Newfoundland and Labrador (NL), Canada, and to highlight patient demographics within this cohort. DESIGN: This retrospective cohort design used population-based pharmacy network prescription data from the province of NL to identify patients who were prescribed opioids from June 1, 2017, to June 1, 2022. SETTING: A cohort of adult and pediatric patients who were being prescribed opioids from June 1, 2017, to June 1, 2022, in NL. PARTICIPANTS: Patients who were prescribed opioids from June 1, 2017, to June 1, 2022. Prescriptions without complete data and medications taken for pain control that were not defined as opioids were excluded from the analysis. Buprenorphine, buprenorphine-naloxone, and methadone were also excluded from the analysis, as these are often prescribed as a treatment for opioid use disorder. RESULTS: Between 27,344 (5.2 percent of NL population) and 57,562 (11 percent of NL population) opioid pain patients in NL were identified from 2017 to 2022, with 2018 having the highest number of opioid pain patients (11 percent). During this period, patients with opioid prescriptions averaged from 55 to 58 years of age. Data also showed more female users of prescription opioids than males, and there were no significant differences between urban and rural locations. The most prevalent type of prescriber during the period of observation was general practitioners (n = 1,131), followed by pharmacists (n = 476) and dentists (n = 237). CONCLUSIONS: In comparison to national averages in Canada, NL had lower prescription opioid use rates. This study acts as a first step to better understand opioid use and prescribing practices in NL.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".