Awareness, predictors and outcomes of drug alerts among people who access harm reduction services in British Columbia, Canada: findings from a 2021 cross-sectional survey
Bibliographic record
Abstract
OBJECTIVES: To assess the awareness and predictors of seeing/hearing a drug alert in British Columbia (BC) and subsequent drug use behaviour after seeing/hearing an alert. METHODS: This study analysed the 2021 BC harm reduction client survey (HRCS)-a cross-sectional self-reported survey administered at harm reduction sites throughout the province and completed by participants using the services. RESULTS: In total, n=537 respondents participated and n=482 (89.8%) responded to the question asking if they saw/heard a drug alert. Of those, n=300 (62.2%) stated that they saw/heard a drug alert and almost half reported hearing from a friend or peer network; the majority (67.4%) reported altering their drug use behaviour to be safer after seeing/hearing a drug alert. The proportion of individuals who saw/heard a drug alert increased with each ascending age category. Among health authorities, there were significant differences in the odds of seeing/hearing an alert. In the past 6 months, the odds of participants who attended harm reduction sites a few times per month seeing/hearing an alert were 2.73 (95% CI: 1.17 to 6.52) times the odds of those who did not. Those who attended more frequently were less likely to report seeing/hearing a drug alert. The odds of those who witnessed an opioid-related overdose in the past 6 months seeing/hearing an alert were 1.96 (95% CI: 0.86 to 4.50) times the odds of those who had not. CONCLUSION: We found that drug alerts were mostly disseminated through communication with friends or peers and that most participants altered their drug use behaviour after seeing/hearing a drug alert. Therefore, drug alerts can play a role in reducing harms from substance use and more work is needed to reach diverse populations, such as younger people, those in differing geographical locations, and those who attend harm reduction sites more frequently.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".