Evaluation of 6 remote First Nations community-based buprenorphine programs in northwestern Ontario: Retrospective study.
Bibliographic record
Abstract
OBJECTIVE: To evaluate established opioid addiction treatment programs that use traditional healing in combination with buprenorphine-naloxone maintenance treatment in 6 First Nations communities in the Sioux Lookout region of northwestern Ontario. DESIGN: Retrospective cohort study. SETTING: Six First Nations communities in northwestern Ontario. PARTICIPANTS: A total of 526 First Nations participants in opioid-dependence treatment programs. INTERVENTION: Buprenorphine-naloxone substitution therapy and First Nations healing programming. MAIN OUTCOME MEASURES: Retention rates and urine drug screening (UDS) results. RESULTS: Treatment retention rates at 6, 12, and 18 months were 84%, 78%, and 72%, respectively. We estimate that the rate at 24 months will also be more than 70%. The UDS programming varied and was implemented in only 1 community. Initially urine testing was voluntary and it then became mandatory. Screening with either method found the proportion of urine samples with negative results for illicit opioids ranged between 84% and 95%. CONCLUSION: The program's treatment retention rates and negative UDS results were higher than those reported for most methadone and buprenorphine-naloxone programs, despite a patient population where severe posttraumatic stress disorder is endemic, and despite the programs' lack of resources and addiction expertise. Community-based programs like these overcome the initial challenge of cultural competence. First Nations communities in other provinces should establish their own buprenorphinenaloxone programs, using local primary care physicians as prescribers. Sustainable core funding is needed for programming, long-term aftercare, and trauma recovery for such initiatives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".