Defining terminology and outcome measures for evaluating overdose response technology: An international Delphi study
Bibliographic record
Abstract
INTRODUCTION: Various novel harm reduction services leverage technology to reduce the rising number of drug poisoning deaths, particularly among those who use drugs alone. There is significant variability in terminology and outcome measures in reporting these interventions, complicating efforts to build a comprehensive knowledge base. Thus, we conducted a Delphi study to establish consensus and heterogeneity in these metrics. METHODS: Panellists from three stakeholder groups (people who use drugs, virtual harm reduction service operators and academics) participated in a multi-round Delphi study. The first round included open-ended questions to propose items in three categories: terminology, demographic information and outcomes. Subsequent rounds included options from a previously conducted scoping review for consideration. Likert ratings were used to achieve consensus, with a 70% threshold. Final rounds involved ranking terminology that reached a consensus. RESULTS: Of 23 initial participants, 14 completed the fourth survey round. "Overdose response technology" was identified as the most appropriate term for these harm reduction technologies. This definition includes drug contamination alerts, overdose response hotlines and applications, wearable overdose detection technology and overdose detection tools. Fourteen demographic outcomes reached a consensus for data collection, including name or handle, neighbourhood, age, gender, past overdose experience, substance used, amount and route of use. Six service use outcomes were recommended: response type, service outcomes, morbidity and mortality, overdose events, responder arrival time and post-rescue care. DISCUSSION AND CONCLUSIONS: The study results are recommended to standardise terminology and guide future research and knowledge dissemination in the field, ensuring clear communication with a shared language.
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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 | 0.378 | 0.295 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".