Virtual opioid poisoning education and naloxone distribution programs: A scoping review
Bibliographic record
Abstract
The global opioid poisoning crisis is a complex issue with far-reaching public health implications. Opioid Poisoning Education and Naloxone Distribution (OPEND) programs aim to reduce stigma and promote harm reduction strategies, enhancing participants' ability to apply life-saving interventions, including naloxone administration and cardiopulmonary resuscitation (CPR) to opioid poisoning. While virtual OPEND programs have shown promise in improving knowledge about opioid poisoning response, their implementation and evaluation have been limited. The COVID-19 pandemic has sparked renewed interest in virtual health services, including OPEND programs. Our study reviews the literature on fully virtual OPEND programs worldwide. We analyzed 7,722 articles, 30 of which met our inclusion criteria. We extracted and synthesized information about the interventions' type, content, duration, the scales used, and key findings. Our search shows a diversity of interventions being implemented, with different study designs, duration, outcomes, scales, and different time points for measurement, all of which hinder a meaningful analysis of interventions' effectiveness. Despite this, virtual OPEND programs appear effective in increasing knowledge, confidence, and preparedness to respond to opioid poisoning while improving stigma regarding people who use opioids. This effect appears to be true in a wide variety of populations but is significantly relevant when focused on laypersons. Despite increasing efforts, access remains an issue, with most interventions addressing White people in urban areas. Our findings offer valuable insights for the design, implementation, and evaluation of future virtual OPEND programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".