Access and barriers to safer supply prescribing during a toxic drug emergency: a mixed methods study of implementation in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: In March 2020, British Columbia, Canada, introduced prescribed safer supply involving the distribution of pharmaceutical grade alternatives to the unregulated toxic drug supply. Prior research has demonstrated positive impacts on overdose mortality, but with limited reach to people who use substances. Objectives of this study were to (1) identify barriers to accessing safer supply prescribing among people who use substances; and (2) determine whether and how barriers differed between people with and without prescriptions, and between urban and rural settings. METHODS: We conducted a participatory mixed-methods study guided by the Consolidated Framework for Implementation Research. Participants (≥ 19 years old) had received a safer supply prescription or were seeking one (survey n = 353; interviews n = 54). RESULTS: Participants who had a prescription were more likely to be living in a large urban centre, compared to medium/smaller centres and rural areas (78.5% vs. 65.8%, standardized mean difference = 0.286). Participants who did not have a prescription were more likely to report an array of structural, interpersonal, and health-related barriers (compared to those who had a prescription). In interviews, participants linked experiences of barriers to stigma and criminalization, low availability of services, lack of information and prescribers, not being able to get what they need, and anxieties, worries and doubts stemming from personal circumstances. There were no notable differences between large urban centres and medium/smaller centres and rural areas in the presence of specific types of barriers. CONCLUSIONS: Findings demonstrate restricted access to safer supply prescribing outside of large urban centres and provide future targets for enhancing implementation. Attention is needed to promote equity and counter systemic barriers in the implementation of responses to the ongoing toxic drug emergency.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 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".