Associations with experience of non-fatal opioid overdose in British Columbia, Canada: a repeated cross sectional survey study
Bibliographic record
Abstract
INTRODUCTION: Lives lost in North America due to the unregulated drug poisoning emergency are preventable and those who survive an opioid overdose may suffer long-term disability. Rates of opioid overdose more than doubled following the onset of the COVID-19 pandemic in British Columbia, Canada. MATERIALS AND METHODS: Our analytical sample was comprised of 1447 participants from the 2018, 2019, and 2021 Harm Reduction Client Survey who responded yes or no to having experienced an opioid overdose in the past 6 months. Participants were recruited from harm reduction sites from across British Columbia. We used logistic regression to explore associations of experiencing an opioid overdose. RESULTS: Overall, 21.8% of participants reported experiencing an opioid overdose in the last six months (18.2% in 2019 and 26.6% in 2021). The following factors were positively associated with increased adjusted odds of experiencing a non-fatal opioid overdose: cis men relative to cis women (AOR 1.49, 95% CI 1.10-2.02), unstably housed compared to people with stable housing (AOR 1.87, 95% CI 1.40-2.50), and participants from 2021 compared to those from 2019 (AOR 3.06, 95% CI 1.57-5.97). The effects of both previous experience of a stimulant overdose and having witnessed an opioid overdose depended on the year of study, with both effects decreasing over subsequent years. CONCLUSIONS: Overdoses have increased over time; in 2021 more than one in four participants experienced an overdose. There is an urgent need for policy and program development to meaningfully address the unregulated drug poisoning emergency through acceptable life-saving interventions and services to prevent overdoses and support overdose survivors.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".