“Oh, Another Overdose, for the Love of Pete”: First Responder Perspectives on Overdose Response Technology
Bibliographic record
Abstract
BACKGROUND: Overdose response applications and hotlines are novel overdose response technologies (ORT)/virtual harm reduction strategies that have recently emerged as a strategy to reduce the harms associated with the ongoing opioid epidemic. First responders are often the first point of contact for people who have overdosed and play a significant role in responses enacted by these services. In this study our aim was to explore the attitudes and perceptions of first responders on these novel technologies. METHODS: We recruited 17 participants using purposive sampling through the province of Alberta between February-April 2023 including 11 paramedics, two firefighters, and five emergency communications operators. To be included in the study, participants were required to be older than 18 years of age, have the ability to communicate effectively in English, provide verbal informed consent, and work in an emergency responder role. Semi-structured interviews were conducted by two evaluators. When reviewing interview transcripts we used thematic analysis to identify key themes and subthemes. RESULTS: Participants discussed their current operating procedures, their current perspectives on overdose response hotlines and apps, how they would best integrate them into their current workloads, and how to raise awareness of these services within first-responder communities. Participants were apprehensive about the integration of these services into their current workloads, including their potential benefits, and raised concerns about their efficacy within communities of people who use drugs. Key strategies were raised for the successful integration of these services into emergency responses including providing information to clients and the feasibility of overdose responses by the general public. CONCLUSION: This study's results add to the existing literature on the toll of the overdose epidemic seen within first-response communities. Furthermore, we explored the communities' diverse perspectives on these novel technologies, including support and concerns, and propose additional strategies for their integration into emergency responses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".