First Aid Education for Opioid Overdose Poisoning: Scoping Review
Bibliographic record
Abstract
Effective health education needs ongoing evidence to support policy development and action in a public health crisis, like the opioid epidemic in the United States. Opioid Education and Naloxone Distribution (OEND) programs work to change behaviors through information, education, and resources to empower people to prevent and respond to opioid overdose poisonings. In this review, we sought to identify the first aid educational components of OEND to address opioid overdose poisoning, identify gaps in the existing literature, and support the development of future studies that could then be systematically reviewed. From a systematic review that identified 2057 peer-reviewed manuscripts, 59 studies demonstrated that the educational literature is sparse, of low quality, lacks quality measures and effective methodologies, and suffers from self-reported and highly inconsistent endpoints, making outcome comparisons challenging, if not impossible. The reviewed OEND programs generally used a public health/health education approach focusing on people who inject opioids, their family and friends, first responders, and rarely the general public. Depending on the learners, interventions were broken down to those <15, 16-90, and >90 minutes, which categorically showed differences in knowledge and first aid response actions. Only eight studies used comparison groups which provide a slightly higher level of evidence. Reports of survival appeared to positively correlate with naloxone kit distribution. Opportunity exists to develop policies and plans that support individual and community efforts through evidence-based guidelines, particularly to the domains of first aid education, so that educators and organizations can deliver efficacious programming that meets the needs of their learners.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".