Healthcare use among people using methamphetamine in Winnipeg, Canada
Bibliographic record
Abstract
ObjectiveWe examined healthcare use among people using methamphetamine who were connected to a Community Health Centre (CHC) in Winnipeg, Canada. CHCs are primary care clinics designed to provide person-centred, holistic care to people who may be impacted by discrimination and/or oppression. ApproachFrom population-based administrative health data, we identified people with a record of a methamphetamine-related healthcare system encounter from 2013-2020 (cases) and created a comparison group matched on age, sex, and postal code but with no recorded history of using methamphetamine (controls). We narrowed this population to those who visited a CHC at least once. Then, using negative binomial regression models adjusted for sociodemographic characteristics, we produced rate ratios and 95% confidence intervals for primary care visits, CHC visits, emergency service contacts, emergency department (ED) visits, and hospitalizations in the 5 years after the first recorded methamphetamine use. ResultsAdjusted rate ratios showed that healthcare use among CHC-connected people using methamphetamine was higher than among CHC-connected people not using methamphetamine: primary care 1.15 (95% CI 1.07-1.23); CHC 1.17 (95% CI 1.01-1.37); emergency services 6.19 (95% CI 5.46-7.02); ED 3.19 (95% CI 2.92-3.48); hospitalization 2.77 (95% CI 2.46-3.10). Conclusion & ImplicationsBeing connected to a CHC may have facilitated access to other health and social services, but there is still unmet need for high quality, holistic care among people using methamphetamine. This research informs our team’s ongoing efforts to address mental health and addictions concerns across sectors and to promote anti-discriminatory approaches to addictions care in the healthcare system.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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".