Exploring Disparities in Opioid Utilization: An Analysis Comparing Red River Métis to All Other Manitobans
Bibliographic record
Abstract
Objective and ApproachTo address the opioid crisis within the Red River Métis (RRM) Community, understanding opioid use is crucial to empower regional health authorities to adapt health programs, services, and policies to meet their unique needs effectively. The investigation utilized focus groups with a Community-Based Participatory Research and Collective Consensual Data Analytics Procedure (CBPR/CCDAP) approach. Additionally, a population-based retrospective cross-sectional study for fiscal years 2006/07–2018/19 was conducted using administrative data from a population research data repository. Rates of prescription opioid dispensing (RPOD) and mean morphine equivalents (MEQ) were compared between RRM and all other Manitobans (AOM) aged 10 years or older. ResultsThe rate of prescription opioid dispensing and MEQ/person were found to be consistently higher among RRM compared to AOM in each study year (p < 0.001). While the RPOD declined among AOM over the study period, it did not change among RRM. Key findings revealed RRM were concerned about how opioids impacted their communities, and felt the need for increased addiction treatment resources, including Red River Métis culture-specific programs. ConclusionThe evidence demonstrates higher RPOD and MEQ among RRM compared to AOM, suggesting elevated risk of opioid-related harms. Focus group feedback reinforces the need for tailored interventions. ImplicationsFuture policies and programs targeting the opioid crisis should prioritize the unique needs of populations like the RRM, requiring tailored, culturally appropriate interventions for effective crisis management.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".