Take-Home Naloxone Programs for Suspected Opioid Overdose in Community Settings: A Scoping Umbrella Review
Bibliographic record
Abstract
Abstract Background: Opioid related overdoses and overdose deaths continue to constitute an urgent public health crisis. The implementation of naloxone programs, such as ‘take-home naloxone’ (THN), has emerged as a key intervention in reducing opioid overdose deaths. These programs aim to train individuals at risk of witnessing or experiencing an opioid overdose to recognize an opioid overdose and respond with naloxone. Naloxone effectively reverses opioid overdoses on a biological level; however, there are outstanding questions on community THN program effectiveness (adverse events, dosing requirements, dose-response between routes of administration) and implementation (accessibility, availability, and affordability). The objective of this scoping review is to identify existing systematic reviews and best practice guidelines relevant to clinical and operational guidance on the distribution of THN. Methods: Using the Arksey & O’Malley framework for scoping reviews, we searched both academic literature and grey literature databases using keywords (Naloxone) AND (Overdose) AND (Guideline OR Review OR Recommendation OR Toolkit). Only documents which had a structured review of evidence and/or provided summaries or recommendations based on evidence were included (systematic reviews, meta-analyses, scoping reviews, short-cut or rapid reviews, practice/clinical guidelines, and reports). Data were extracted from selected evidence in two key areas: (1) study identifiers; and (2) methodological characteristics. Results: A total of 47 articles met inclusion criteria: 20 systematic review; 10 grey literature articles; 8 short-cut or rapid reviews; 4 scoping reviews; and 5 other review types (e.g. mapping review and comprehensive reviews). The most common subject themes were: naloxone effectiveness, safety, provision feasibility/acceptability of naloxone distribution, dosing and routes of administration, overdose response after naloxone administration, cost-effectiveness, naloxone training and education, and recommendations for policy, practice and gaps in knowledge. Conclusions: Several recent systematic reviews address the effectiveness of take-home naloxone programs, naloxone dosing/route of administration, and naloxone provision models. Gaps remain in the evidence around evaluating cost-effectiveness, training parameters and strategies, and adverse events following naloxone administration. As THN programs continue to expand in response to opioid overdose deaths, this review will contribute to understanding the evidence 60 base for policy and THN program development and expansion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".