Description and evaluation of a national humanitarian opioid poisoning education and naloxone distribution program
Bibliographic record
Abstract
SETTING: Canada's opioid poisoning crisis claimed 49,105 lives from January 2016 to June 2024. Opioid poisoning education and naloxone distribution programs can reduce fatalities, although access remains inconsistent across Canada. These programs have mostly been delivered in person through community, healthcare, and social service agencies. INTERVENTION: The Canadian Red Cross implemented a national, free, bilingual, virtually accessible, opioid harm reduction program, leveraging its experience in first aid education and community relationships as a humanitarian organization. The Opioid Harm Reduction program launched three new courses and added opioid poisoning content to four existing courses. Courses were adapted continually based on the feedback of people with lived experience of drug use and program participants. The program was delivered from January 2021 to March 2024 and evaluated through quantitative and qualitative methods. OUTCOMES: The program delivered 1,386,995 trainings and successfully reached diverse groups, including those from Indigenous (5.3%) and rural (25.2%) communities, but had an underrepresentation of men (34.3%) and individuals working in the construction industry (4.8%). Participants' self-reported knowledge and confidence in responding to opioid poisoning increased across all courses (p < 0.001), particularly for learners without prior training. In total, 24,098 intranasal naloxone kits were distributed, 60.4% to Ontario, Manitoba, and British Columbia. Most participant feedback (82%) received was positive, highlighting the course's simplicity and focus on stigma. IMPLICATIONS: The Canadian Red Cross Opioid Harm Reduction program advanced harm reduction, increased awareness of opioid poisonings, and situated the response to the opioid poisoning crisis as a community health effort.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".