Patterns of prescription opioid dispensing among Red River Métis in Manitoba, Canada: a retrospective longitudinal cross-sectional study
Bibliographic record
Abstract
] Hospital admissions due to opioid poisonings increased by 5% from 2016 to 2018, with 17.6 per 100 000 people admitted to hospital in 2018. The highly addictive nature of opioids and often inappropriate opioid prescribing practices are major contributors to the worsening opioid crisis. ertain Canadian populations are at high risk for being negatively affected by prescribed opioids. Indigenous people have been shown to have higher rates of hospital admission due to opioid poisoning than other Canadians. 1 However, there is limited information on the impact of the opioid crisis among Mtis specifically. The Red River Mtis are descendants of First Nations and European settlers who once governed a distinct nation in the northwest part of North America. Canadian colonial laws and policies dispossessed the Red River Mtis of their lands and subjected them to many other damaging injustices. Despite these challenges, the Red River Mtis remain resilient and resourceful, celebrating a rich cultural and social history, 6 and they maintain their rights to self-determination and self-government.
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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 | 0.000 | 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 | 0.001 |
| 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".