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
Abstract Introduction: Unregulated supply of fentanyl and adulterants continues to fuel the opioid epidemic across the globe. Mobile Overdose Response Services (MORS) are novel technologies that offer virtual supervised consumption (including hotline and mobile applications) to minimize the risk of fatal overdose for those who are unable to access a physical supervised consumption site. However, as newly implemented services, they are also faced with numerous limitations. The objective of this study was to examine the facilitators and barriers to the adoption and implementation of MORS in the context of the current drug poisoning crisis in Canada. Methods: A total of 64 semi-structured interviews were conducted between November 2021 and April 2022. Participants consisted of individuals with lived or living experience of substance use (i.e. peers), family members, health care providers, harm reduction workers, members of the general public, and MORS operators. Inductive thematic analysis informed by grounded theory was used to identify major themes and subthemes. Results: Respondents revealed that MORS facilitated a safe, trauma-informed 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. However, barriers to adoption of MORS included uncertainty of funding, lacking marketing strategies, and compassion fatigue amongst the workers. Conclusion: Although MORS were viewed as a useful addition to the currently existing harm reduction toolkit, its barriers to adoption must be continuously examined and monitored in various contexts 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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.007 |
| 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".