Protocol for a mixed-methods feasibility study for the surviving opioid overdose with naloxone education and resuscitation (SOONER) randomised control trial
Bibliographic record
Abstract
INTRODUCTION: The surviving opioid overdose with naloxone education and resuscitation (SOONER) project uses co-design and trial methods to develop and evaluate a point-of-care overdose education and naloxone distribution (OEND) tool. We plan to conduct a randomised controlled trial to assess the effectiveness of our OEND tool in comparison with best available standard of care by observing participants' performance as a responder to a simulated overdose. Recruiting and retaining people at risk of or likely to witness opioid overdose raises scientific, logistical and bioethical challenges. A feasibility study is needed to establish the effectiveness of recruitment and retention strategies and acceptability of study procedures prior to launching the full trial. METHODS AND ANALYSIS: Strategies to enhance recruitment include candidate-driven recruitment, verbal informed consent, and attractive, destigmatising materials. Adults at risk of or likely to witness opioid overdose will be recruited through an urban emergency department, inpatient and ambulatory addiction medicine service, and outpatient family practice settings. Participants randomised to the intervention arm will receive our OEND intervention; those in the control arm will be referred to existing OEND programme. Retention procedures include participant reminders, flexible scheduling, cash and comfort compensation, and strategies to maintain a consistent relationship between individual study staff and participants. Within 2 weeks following recruitment, participants will engage as a responder to a manikin-simulated overdose, and complete overdose knowledge and attitudes questionnaires. The primary outcome is recruitment and retention feasibility, defined as the recruitment of 28 participants within 28 days of recruitment and <50% attrition at the overdose simulation. Staff and participant feedback will also be collected and considered. ETHICS AND DISSEMINATION: The study has been reviewed by ethics boards at St. Michael's Hospital, Toronto Public Health and the University of Toronto. Dissemination will occur through peer-reviewed publication and presentations. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov registry (NCT03821649).
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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 | 0.068 | 0.083 |
| Meta-epidemiology (narrow) | 0.008 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.203 | 0.041 |
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".