Perspectives of Key Partners on Improving Awareness of Virtual Harm Reduction Services: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Supervised Consumption Sites (SCS) have proven effective in reducing overdose-related deaths by providing safe spaces for people who use substances. However, barriers such as stigma, operating hours, and travel distance can limit access to SCS. Virtual harm reduction services such as phone-based overdose response hotlines and apps have emerged as an alternative when SCS access is hindered. These collectively have also been named Mobile Overdose Response Services (MORS). At this time, little is known about how best to increase awareness of these services. MATERIALS AND METHODS: For this qualitative study, 46 individuals across Canada were recruited to examine ways to improve awareness of virtual harm reduction. Semi-structured interviews with the participants were conducted. Data analysis using inductive thematic analysis informed by grounded theory was used to identify major themes. RESULTS: Participants identified enhanced social marketing as a priority to raise awareness and reduce the stigma associated with substance use and MORS. Social media campaigns, endorsements from peers and healthcare professionals, and community support were suggested marketing strategies. The study revealed the importance of connecting with existing resources and services, including outreach teams, to improve MORS penetration. A cohesive system and reference lists were advocated for smoother access and navigation. CONCLUSION: This study offers insights into key partners' perspectives and recommendations around increasing overdose response hotline and app awareness, thereby contributing to user harm reduction efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".