Implementation Barriers and Outcomes of a Provincial Prescribed Safer Supply Initiative among Indigenous People who use Substances amid COVID-19 and a Toxic Drug Crisis in British Columbia, Canada
Bibliographic record
Abstract
Background: In 2020, British Columbia (BC) introduced Risk Mitigation Guidance to support the provision of prescribed safer supply (PSS) to people at risk of toxic drug harms and death amid the COVID-19 pandemic. We sought to assess the self-reported barriers to and outcomes of PSS among Indigenous people who sought or accessed a prescription. Methods: Cross-sectional and longitudinal surveys with up to 5 time points per participant over 3 months were used to collect data on health, health care, and systemic barriers. Outcome measures included health-related quality of life, psychosocial well-being, and behaviours related to unregulated substance use, COVID-19 protection, and criminalized income generation. Generalized estimating equations were used to assess the effects of time-varying PSS exposure on outcomes over the follow-up period. Results: Among the total sample (n=131), past negative experiences (64%), drug use stigma, (51%), mistrust (49%), negative reactions from health care providers (48%), and lack of privacy (46%) were reported in health care settings. Among the longitudinal cohort (n=102), there was no evidence of an effect of prescription receipt on change in any outcome over time or over the 12-week follow-up period, independent of prescription status, apart from COVID-19 protective behaviours, which showed a small increase over time (adj. β = 0.0090, 95% CI = 0.0043, 0.014). Conclusion: Limitations of the early implementation of PSS in BC included its medicalized delivery and lack of wraparound supports. Further work must be done to ensure the effectiveness, relevancy, and safety of prescribed safer supply for Indigenous people.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".