Perspectives of Canadian Healthcare and Harm Reduction Workers on Mobile Overdose Response Services: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Supervised consumption sites (SCS) are an evidence-based intervention proven effective for preventing drug overdose deaths. Obstacles to accessing SCS include stigma, limited hours of operation, concerns about policing, and limited geographic availability. Mobile overdose response services (MORS) are novel technologies that provide virtual supervised consumption to help reduce the risk of fatal overdoses, especially for those who use alone. MORS can take various forms, such as phone-based hotlines and mobile apps. The aim of this article is to assess the perceptions of MORS among healthcare and harm reduction staff to determine if they would be comfortable educating clients about these services. METHODS: Twenty-two healthcare and harm reduction staff were recruited from Canada using convenience, snowball, and purposive sampling techniques to complete semistructured interviews. Inductive thematic analysis informed by grounded theory was used to identify main themes and subthemes. RESULTS: Four themes were identified: (1) increasing MORS awareness among healthcare providers was seen as useful; (2) MORS might lessen the burden of drug overdoses on the healthcare system but could also increase ambulance callouts; (3) MORS would benefit from certain improvements such as providing harm reduction resources and other supports; and (4) MORS are viewed as supplements for harm reduction, but SCS were preferred. CONCLUSIONS: This research provides valuable perspectives from healthcare and harm reduction workers to understand their perception of MORS and identifies key areas of potential improvement. Practical initiatives to improve MORS implementation outcomes exist.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.037 | 0.014 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".