Understanding the barriers and facilitators to implementing and sustaining Mobile Overdose Response Services from the perspective of Canadian key interest groups: a qualitative study
Bibliographic record
Abstract
INTRODUCTION: Unregulated supply of fentanyl and adulterants continues to drive the overdose crisis. Mobile Overdose Response Services (MORS) are novel technologies that offer virtual supervised consumption to minimize the risk of fatal overdose for those who are unable to access other forms of harm reduction. However, as newly implemented services, they are also faced with numerous limitations. The aim of this study was to examine the facilitators and barriers to the adoption of MORS in Canada. METHODS: A total of 64 semi-structured interviews were conducted between November 2021 and April 2022. Participants consisted of people who use substances (PWUS), family members of PWUS, health care professionals, harm reduction workers, MORS operators, and members of the general public. Inductive thematic analysis was used to identify the major themes and subthemes. RESULTS: Respondents revealed that MORS facilitated a safe, anonymous, and nonjudgmental environment for PWUS to seek harm reduction and other necessary support. It also created a new sense of purpose for operators to positively contribute to the community. Further advertising and promotional efforts were deemed important to increase its awareness. However, barriers to MORS implementation included concerns regarding privacy/confidentiality, uncertainty of funding, and compassion fatigue among the operators. CONCLUSION: Although MORS were generally viewed as a useful addition to the currently existing harm reduction services, it's important to monitor and tackle these barriers by engaging the perspectives of key interest groups.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".