What Is Known about Community Pharmacy-Based Take-Home Naloxone Programs and Program Interventions? A Scoping Review
Bibliographic record
Abstract
A variety of new sources describing community pharmacy-based take-home naloxone (THN) programs have emerged recently in the literature. There is a need to define the types of take-home naloxone programs being offered to support future research designs in implementing and evaluating standardized programs that fill pharmacist and patient knowledge gaps and lift current barriers for optimal community pharmacy naloxone provision. The objective of this paper is to summarize the literature on community pharmacy-based THN programs, including specific program interventions used to increase naloxone dispensing, naloxone availability and dispensing patterns, facilitators and barriers for the THN programs, and knowledge gaps. Online databases such as PubMed, EMBASE, Scopus, and International Pharmaceutical Abstracts (IPA) and a search of the grey literature were used to identify eligible sources. Sources were screened by two reviewers for eligibility in COVIDENCE software. Both reviewers compared screening results and resolved conflicts through discussion. A data extraction form for all identified full texts was completed by both reviewers and results were compiled through reviewer discussion. Fifty-two sources met the eligibility criteria. The top three barriers identified were: cost/coverage of naloxone, stigma, and education/training for pharmacists. THN program interventions included screening tools, checklists, pocket cards, patient brochures, and utilizing the pharmacy management system to flag eligible patients. Patient knowledge gaps included naloxone misinformation and lack of awareness, while pharmacists demonstrated administrative, clinical, and counselling knowledge gaps. Naloxone availability was found to be highly variable, where independent and rural pharmacies were less likely to stock or dispense naloxone. Further, pharmacies located in districts with higher rates of opioid overdose deaths and lower household income were also less likely to have naloxone available. This review identified multiple new programs, showcasing that the implementation and evaluation of THN programs are an expanding area of research. Future research should focus on implementing and evaluating a THN program through a randomized controlled trial design that incorporates solutions for the barriers and knowledge gaps identified in this study.
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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 | 0.026 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".