Factors associated with obtaining prescribed safer supply among people accessing harm reduction services: findings from a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: With growing rates of unregulated drug toxicity death and concerns regarding COVID-19 transmission among people who use drugs, in March 2020, prescribed safer supply guidance was released in British Columbia. This study describes demographic and substance use characteristics associated with obtaining prescribed safer supply and examines the association between last 6-month harm reduction service access and obtaining prescribed safer supply. METHODS: Data come from the 2021 Harm Reduction Client Survey administered at 17 harm reduction sites across British Columbia. The sample included all who self-reported use of opioids, stimulants, or benzodiazepines in the prior 3 days (N = 491), given active use of these drugs was a requirement for eligibility for prescribed safer supply. The dependent variable was obtaining a prescribed safer supply prescription (Yes vs. No). The primary independent variables were access to drug checking services and access to overdose prevention services in the last 6 months (Yes vs. No). Descriptive statistics (Chi-square tests) were used to compare the characteristics of people who did and did not obtain a prescribed safer supply prescription. Multivariable logistic regression models were run to examine the association of drug checking services and overdose prevention services access with obtaining prescribed safer supply. RESULTS: A small proportion (n = 81(16.5%)) of the sample obtained prescribed safer supply. After adjusting for gender, age, and urbanicity, people who reported drug checking services access in the last 6 months had 1.67 (95% CI 1.00-2.79) times the odds of obtaining prescribed safer supply compared to people who had not contacted these services, and people who reported last 6 months of overdose prevention services access had more than twice the odds (OR 2.08 (95% CI 1.20-3.60)) of prescribed safer supply access, compared to people who did not access these services. CONCLUSIONS: Overall, the proportion of respondents who received prescribed safer supply was low, suggesting that this intervention is not reaching all those in need. Harm reduction services may serve as a point of contact for referral to prescribed safer supply. Additional outreach strategies and service models are needed to improve the accessibility of harm reduction services and of prescribed safer supply in British Columbia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".