Implementation of risk mitigation prescribing during dual public health emergencies: A qualitative study among Indigenous people who use drugs and health planners in Northern British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: In response to the dual public health emergencies of COVID-19 and the overdose crisis, the Government of British Columbia (BC) introduced risk mitigation prescribing, or prescribed safer supply. In the context of colonialism and racism, Indigenous people are disproportionately impacted by substance use harms and experience significant barriers to receiving care, particularly those living in rural and remote communities. As part of a larger provincial evaluation, we sought to assess the implementation of risk mitigation prescribing as experienced by Indigenous people who use drugs (IPWUD) in Northern BC. METHODS: We used the Consolidated Framework for Implementation Research and the First Nations Perspective on Health and Wellness as conceptual frameworks to guide the study. In partnership with people with lived/living experience, we conducted 20 qualitative interviews with IPWUD. Data were supplemented by four interviews with health planners and analyzed thematically. RESULTS: Participants reported limited implementation of risk mitigation prescribing in Northern BC, with unique regional challenges and innovative facilitators to access. Analysis of supplementary health planner data was consistent with the experiences of IPWUD and together provided a comprehensive picture of implementation in Northern BC. Four themes emerged: 1) Northern socio-politico-cultural barriers to implementation (outer setting), 2) rural and remote healthcare delivery challenges (inner setting), 3) adaptability of risk mitigation prescribing on Northern wellness (intervention characteristics), and 4) Northern ingenuity, relationality and champions facilitating access (implementation process). CONCLUSIONS: Implementation and access to risk mitigation prescribing in Northern BC was limited, with region-specific applicability challenges and a health service delivery model that was not able to sufficiently meet the unique service needs of IPWUD. Demonstrating Northern ingenuity, peer groups, harm reduction community champions, and telehealth services were identified as stopgap measures that promoted access and reduced inequitable implementation within the region.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".