Using illicit drugs alone in Vancouver, Canada: a gender-based analysis
Bibliographic record
Abstract
OBJECTIVES: Canada continues to experience an epidemic of toxic drug-related overdose deaths. Public health messaging emphasizes the dangers of using drugs alone as it restricts timely overdose response or renders it impossible, yet this practice remains prevalent among people who use drugs. While drug use practices and associated harms are known to be highly gendered, little is known about how factors shaping solitary drug use may differ across genders (including cisgender men, cisgender women, transgender women, Two-Spirit people and gender diverse people). Thus, we sought to explore solitary drug use practices according to gender in Vancouver, Canada. METHODS: Data were collected through Vancouver Injection Drug Users Study, a prospective cohort study between June 2019 and May 2023. We used gender-stratified multivariable generalized estimating equation models to identify factors associated with using drugs alone. RESULTS: Among the 697 participants, 297 (42.6%) reported using drugs alone in the previous 6 months at baseline. In multivariable analyses, we found that being in a relationship was negatively associated with using alone for both cisgender men and cisgender women (adjusted odds ratio [AOR] = 0.25 and 0.34, respectively), while homelessness was negatively associated for cisgender men only (AOR = 0.45). Factors positively associated for cisgender men included daily illicit stimulant use (AOR = 1.90), and binge drug use (AOR = 2.18). For cisgender women, only depression was positively associated with using drugs alone (AOR = 2.16). All p-values < 0.05. While unable to conduct a multivariable analysis on transgender, Two-Spirit and gender diverse people due to small sample sizes, bivariate analyses showed larger impact of depression on using alone for Two-Spirit (OR = 8.00) and gender diverse people (OR = 5.05) compared to others, and only gender diverse people's risk was impacted by experiences of violence (OR = 9.63). All p-values < 0.05. CONCLUSION: The findings of this study suggest significant heterogeneity in gender-specific factors associated with using drugs alone. Factors exclusively impacting cisgender men's risk included homelessness and daily stimulant use, and depression having a significant impact on cisgender women's, but not cisgender men's, risk. Ultimately, gender-specific factors must be recognized in public health messaging, and in developing policies and harm reduction measures to address the risks associated with using alone.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".