Supervised smoking facility access, harm reduction practices, and substance use changes during the COVID-19 pandemic: a community-engaged cross-sectional study
Bibliographic record
Abstract
Background The potential public health benefits of supervised smoking facilities (SSFs) are considerable, and yet implementation of SSFs in North America has been slow. We conducted this study to respond to significant knowledge gaps surrounding SSF utilization and to characterize substance use, harm reduction practices, and service utilization following the onset of the COVID-19 pandemic. Methods A questionnaire was self-administered at a single site by 175 clients using an outdoor SSF in Vancouver, Canada, between October–December 2020. Questionnaire responses were summarized using descriptive statistics. Multinomial logistic regression techniques were used to examine factors associated with increased SSF utilization. Results Almost all respondents reported daily substance use (93% daily use of opioids; 74% stimulants). Most used opioids (85%) and/or methamphetamine (66%) on the day of their visit to the SSF. Respondents reported drug use practice changes at the onset of COVID-19 to reduce harm, including using supervised consumption sites, not sharing equipment, accessing medically prescribed alternatives, cleaning supplies and surfaces, and stocking up on harm reduction supplies. Importantly, 45% of SSF clients reported using the SSF more often since the start of COVID-19 with 65.2% reporting daily use of the site. Increased substance use was associated with increased use of the SSF, after controlling for covariates. Conclusions Clients of the SSF reported increasing not only their substance use, but also their SSF utilization and harm reduction practices following the onset of COVID-19. Increased scope and scale of SSF services to meet these needs are necessary.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".