Determinants of psychological distress during the <scp>COVID</scp>‐19 pandemic among people who use drugs in Montreal, Canada
Bibliographic record
Abstract
INTRODUCTION: Limited data exists on psychological impacts of the COVID-19 pandemic among people who use drugs (PWUD). This study aimed to determine the prevalence and correlates of severe psychological distress (PD) among PWUD in Montreal around the beginning of the pandemic. METHODS: We conducted a rapid assessment study from May to December 2020 among PWUD recruited via a community-based cohort of people who inject drugs in Montreal (Hepatitis C cohort [HEPCO], N = 128) and community organisations (N = 98). We analysed self-reported data on changes in drug use behaviours and social determinants since the declaration of COVID-19 as a public health emergency, and assessed past-month PD using the Kessler K6 scale. Multivariable logistic regression was conducted to examine correlates of PD distress (score ≥13). RESULTS: Of 226 survey participants, a quarter (n = 56) were screened positive for severe PD. In multivariable analyses, age (1-year increment) (adjusted odds ratio = 0.94, 95% confidence interval [0.90, 0.98]) and a decrease in non-injection drug use versus no change (0.26 [0.07, 0.92]) were protective against severe PD, while positive associations were found for any alcohol use in the past 6 months (3.73 [1.42, 9.78]), increased food insecurity (2.88 [1.19, 6.93]) and both moving around between neighbourhoods more (8.71 [2.63, 28.88]) and less (3.03 [1.18, 7.74]) often compared to no change. DISCUSSION AND CONCLUSIONS: This study documented a high prevalence of severe PD among PWUD during the COVID-19 pandemic compared with pre-COVID-19 data. Social determinants such as food insecurity and mobility issues, alongside demographic and substance use-related factors, were linked to distress. Evidence-based risk mitigation strategies for this population could reduce negative consequences in future pandemics or disruptions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".