Patterns of prescription opioid dispensing among Red River Métis in Manitoba, Canada: a retrospective longitudinal cross-sectional study.
Bibliographic record
Abstract
BACKGROUND: Amid rising concern about opioid use across Canada, Métis leaders in Manitoba are seeking information on prescription opioid dispensing in Red River Métis populations to assist with planning and implementing appropriate evidence-based harm-reduction strategies in their communities. We examined patterns of prescription opioid dispensing among Red River Métis and compared them to those among other residents of Manitoba. METHODS: We conducted a population-based retrospective cross-sectional study for fiscal years 2006/07-2018/19 using administrative data from the Manitoba Population Research Data Repository and a study designed in partnership with researchers from the Manitoba Métis Federation. We compared age- and sex-adjusted rates of prescription opioid dispensing and mean morphine equivalents (MEQ) between Red River Métis and all other Manitobans aged 10 years or older, in accordance with Indigenous data sovereignty principles. To better understand what was driving any differences in patterns of prescription opioid dispensing between the 2 groups, we stratified the groups by age, sex, urbanicity, number of comorbidities, income quintile and opioid type, and compared patterns in MEQ/person. RESULTS: < 0.001). The rate of prescription opioid dispensing declined and the MEQ/person rose among other Manitobans over the study period but did not change among Red River Métis. INTERPRETATION: The rate of prescription opioid dispensing and the potency of prescribed opioids were higher among Red River Métis in Manitoba than among other Manitobans. Further investigation into the different dispensing patterns between the 2 groups and the potential opioid-related harms they may herald is warranted.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".