Beliefs, attitudes and experiences of virtual overdose monitoring services from the perspectives of people who use substances in Canada: a qualitative study
Bibliographic record
Abstract
Abstract Background Solitary use of substances is a risk factor for substance use-related mortality. Novel e-health harm reduction interventions such as virtual overdose monitoring services (VOMS) have emerged in North America to improve access to emergency overdose support for people who use substances (PWUS). To date, little research has been published, and the perspectives of PWUS are needed to inform evaluation and policy efforts. Objective To explore the beliefs, values and perceptions of PWUS around using and accessing VOMS in Canada. Methods A qualitative study following grounded theory methodology was conducted. Using existing peer networks, purposive and snowball sampling was conducted to recruit PWUS (≥ 18 years) with previous experience with VOMS. Thematic analysis was used to analyze twenty-three interviews. Several methods were employed to enhance rigor, such as independent data coding and triangulation. Results Twenty-three one-on-one telephone interviews of PWUS with previous experience with VOMS were completed and analyzed. The following themes emerged: (1) feelings of optimism around VOMS to save lives; (2) privacy/confidentiality was highly valued due to stigma and fear of arrest; (3) concerns with reliable cell phones negatively impacting VOMS uptake; (4) concerns around emergency response times, specifically in rural/remote communities; (5) desire for trusting relationships with VOMS operators; (6) importance of mental health supports and referrals to psychosocial services; and (7) possible limited uptake due to low public awareness of VOMS. Conclusion This qualitative study provided novel insights about the VOMS from the perspectives of PWUS. PWUS generally felt optimistic about the potential of VOMS as a suitable harm reduction intervention, but several potential barriers around accessing VOMS were identified that may limit uptake. Future research is warranted.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".