Convenience and comfort: reasons reported for using drugs alone among clients of harm reduction sites in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: North American communities are severely impacted by the overdose crisis, particularly in British Columbia (BC), which has the highest toxic drug overdose death rate in Canada. Most fatal overdoses in BC occurred among individuals using alone and in private residences. This study aimed to assess prevalence and reasons for using drugs alone among people accessing harm reduction services in BC. METHODS: We recruited harm reduction supply distribution site clients from 22 communities across BC. Descriptive statistics and multivariable logistic regression were used to describe factors associated with using alone. Thematic analysis of free-text responses providing reasons for using alone were grouped with survey data and additional themes identified. RESULTS: Overall, 75.8% (n = 314) of the study sample (N = 414) reported using drugs alone within the last week. Those that reported using alone did not differ from those that did not by gender, age, urbanicity, or preferred drug use method. Among those that used alone, 73.2% (n = 230) used opioids, 76.8% (n = 241) used crystal meth, 41.4% (n = 130) used crack/cocaine, and 44.6% (n = 140) used alcohol in the past week. Polysubstance use involving stimulants, opioids, and/or benzodiazepines was reported by 68.5% (n = 215) of those that used alone. Additionally, 22.9% (n = 72) of those that used alone had experienced an opioid and/or stimulant overdose in the past 6 months. In a multivariable logistic regression model, having no regular housing and past week crack/cocaine use were associated with using alone (adjusted odds ratio (AOR): 2.27; 95% CI 1.20-4.27 and AOR: 2.10; 95% CI 1.15-3.82, respectively). The most common reason reported for using alone was convenience and comfort of using alone (44.3%). Additional reasons included: stigma/hiding drug use (14.0%); having no one around (11.7%); safety (9.6%); and not wanting to share drugs with others (8.6%). CONCLUSIONS: Using drugs alone, particularly for convenience and comfort, is ubiquitous among people accessing harm reduction services. Overdose prevention measures that go beyond individual behaviour changes, including providing a safer supply of drugs and eliminating stigma, are paramount to mitigate harms. These interventions are especially necessary as emergence of coronavirus disease may further exacerbate unpredictability of illicit drug content and overdose risk.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".