Virtual and remote opioid poisoning education and naloxone distribution programs: a scoping review
Bibliographic record
Abstract
Abstract The opioid poisoning crisis is a complex and multi-faceted global epidemic with far-reaching public health effects. Opioid Poisoning Education and Naloxone Distribution (OPEND) programs destigmatize and legitimize harm reduction measures while increasing participants’ ability to administer naloxone and other life-saving interventions in opioid poisoning emergencies. While virtual OPEND programs existed prior to the COVID-19 pandemic and were shown to be effective in improving knowledge of opioid poisoning response, they were not widely implemented and evaluated. The COVID-19 pandemic brought both urgent and sustained interest in virtual health services, including harm reduction interventions and OPEND programs. We aimed to assess the scope of literature related to fully virtual OPEND programming, with or without naloxone distribution, worldwide. A search of the literature was conducted and yielded 7,722 articles, of which 31 studies fit the inclusion criteria. Type and content of the educational component, duration of training, scales used, and key findings were extracted and synthesized. Our search shows that virtual and remote OPEND programs appear effective in increasing knowledge, confidence, and preparedness to respond to opioid poisoning events while improving stigma regarding people who use substances. This effect is shown to be true in a wide variety of populations but is significantly relevant when focused on laypersons. Interventions ranged from the use of videos, websites, telephone calls, and virtual reality simulations. A lack of consensus was found regarding the duration of the activity and the scales used to measure its effectiveness. Despite increasing efforts, access remains an issue, with most interventions addressing White people in urban areas. These findings provide insights for planning, implementation, and evaluation of future virtual and remote OPEND programs. Author Summary Facing a global health challenge, the opioid poisoning crisis affects individuals across all communities, ages, and socioeconomic groups, leading to high fatality rates. Educational programs addressing opioid poisoning have emerged as life-saving and cost-effective interventions. This review focuses on these programs conducted in a virtual setting, eliminating the need for in-person contact between staff and participants. We have identified and summarized evidence about the outcomes of these programs, which may include naloxone distribution. Our findings offer valuable insights for planning, implementing, and evaluating such programs. Furthermore, we highlight gaps in current knowledge, paving the way for future research.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".