Low awareness of risk mitigation prescribing in response to dual crises of COVID-19 and overdose deaths among people who use unregulated drugs in Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND: When the novel coronavirus pandemic emerged in March 2020, many settings across Canada and the USA were already contending with an existing crisis of drug overdoses due to the toxic unregulated drug supply. In response, the Canadian province of British Columbia (BC) released innovative risk mitigation prescribing (RMP) guidelines for medical professionals to prescribe pharmaceutical alternatives to unregulated drugs in an effort to support the self-isolation of people who use unregulated drugs (PWUD) in preventing both SARS-CoV-2 virus infection and overdoses. We sought to assess the level of awareness of RMP and identify factors associated with this awareness among PWUD in Vancouver, BC. METHODS: Cross-sectional data were derived from participants enrolled in three community-recruited prospective cohort studies of PWUD in Vancouver, interviewed between July and November 2020. Multivariable logistic regression analysis was used to identify factors associated with awareness of RMP. RESULTS: Among 633 participants, 302 (47.7%) had heard of RMP. Of those 302 participants, 199 (65.9%) had never tried to access RMP services, ten (3.3%) made an unsuccessful attempt to access RMP, and 93 (30.8%) received RMP. In the multivariable analysis, participants who had awareness of RMP guidelines were more likely to self-identify as white (adjusted odds ratio [AOR] = 1.47; 95% confidence interval [CI]: 1.01, 2.13), to have completed secondary school education or higher (AOR = 1.67; 95% CI: 1.16, 2.39), to have used drugs at a supervised consumption or overdose prevention site in the past six months (AOR = 1.66; 95% CI: 1.10, 2.52), and to have received opioid agonist therapy as treatment for opioid use disorder in the past six months (AOR = 1.51; 95% CI: 1.02, 2.24). CONCLUSION: At least four months after the release of the guidelines, RMP was known to less than half of our study participants, warranting urgent educational efforts for PWUD, particularly among racialized groups and those who were not accessing other harm reduction services. Furthermore, the majority of participants who were aware of RMP guidelines had never tried to access the service, suggesting the need to improve perceived accessibility and knowledge of eligibility criteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".