Perceptions of overdose response hotlines and applications among rural and remote individuals who use drugs in Canada: a qualitative study
Bibliographic record
Abstract
INTRODUCTION: The overdose epidemic continues to be one of the largest public health crises in Canada. Various harm reduction supports have been implemented to curb this epidemic; however, they remain concentrated within urban settings. To address this limitation, overdose response hotlines and applications (ORHA) are novel, technologybased harm reduction services that may reduce drug-related mortality for people who use substances (PWUS) living in rural communities through virtual supervised consumption. These services enable more timely and remote activation of emergency responses, should an individual become unresponsive. We aimed to explore the experiences, perceptions and attitudes surrounding ORHA of individuals living in rural areas. METHODS: We conducted semistructured interviews with 15 PWUS (7 [46.7%] male, 9 [60%] Indigenous) who lived in rural, remote or Indigenous communities. Interviews were conducted until data saturation was reached. Data were analyzed using thematic analysis. RESULTS: Six key themes emerged: (1) participants viewed ORHA as a pragmatic intervention for rural areas but noted potential limitations to its uptake and effectiveness; (2) rural geography may hinder EMS response times, reducing the efficacy of ORHA; (3) ORHA uptake may be limited due to significant stigma faced by PWUS in these communities; (4) lack of access to technology remains a barrier to ORHA access; (5) harm reduction awareness is often limited in rural communities; and (6) there are unique social implications around substance use and harm reduction for rural Indigenous PWUS. CONCLUSION: While participants believed that ORHA may be a feasible harm reduction strategy for rural PWUS, limitations, including response times, technological access and substance use stigma, remain.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".