Assessing the Safety, User Acceptability, Dissemination, and Reach of a Comprehensive Web-Based Resource on Medications for Opioid Use Disorder (MOUD Hub): Protocol for a Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Medications for opioid use disorder (MOUD), such as methadone and buprenorphine, are the gold standard for opioid use disorder (OUD) treatment. Owing to various barriers, MOUD access and retention are low in the United States. The internet presents a digital solution to mitigate barriers, but a comprehensive and reliable resource is lacking. We present a user-friendly, web-based resource, the MOUD Hub, that provides reliable information on MOUD. OBJECTIVE: This study aims to assess the safety, acceptability, feasibility of dissemination, and reach of the MOUD Hub using focus groups and advertising on 1 key search engine and 1 social media platform. METHODS: This protocol describes the development of the MOUD Hub and the descriptive observational feasibility study that will be undertaken. The MOUD Hub uses motivational interviewing principles to guide users through the stages of change. The website provides evidence-based information from national health and substance use agencies, harm reduction organizations, and peer-reviewed literature. First, pilot focus groups with 10 graduate students who have lived experience with OUD will be conducted to provide feedback on safety concerns. Then, focus groups with 20-30 potential MOUD Hub users (eg, people with OUD with and without MOUD experience, friends and family, and health care providers) will be conducted to assess safety, acceptability, reach, and usability. Data will be analyzed using inductive thematic analysis. The website will be advertised on Google and MOUD-specific Reddit forums to assess dissemination, reach, and user acceptability based on the total user volume, sociodemographic characteristics, pop-up survey responses, and 1-year engagement patterns. This information will be collected through Google Analytics. Potential differences between users from Google and Reddit will be assessed. RESULTS: The MOUD Hub will be launched in January 2025. Data collected from 5 focus groups (approximately 30-40 participants) will be used to improve the website before launching it. There is no target sample size for the second stage of the study as it aims to assess dissemination feasibility and reach. Data will be collected for a year, analyzed every 3 months, and used to improve the website. CONCLUSIONS: The MOUD Hub offers an innovative theory-based approach, tailored to people with OUD and their family and friends, to increase access to and retention in MOUD treatment in the United States and provides broader harm reduction resources for those not currently in a position to receive treatment or those at risk of resuming illicit opioid use. Findings from this feasibility phase will serve to better tailor the MOUD Hub. After modifying the website based on our findings, we will use a randomized controlled trial to assess its efficacy in increasing MOUD access and retention, contributing to growing research on web-based interventions for OUD. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/57065.
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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 | 0.087 | 0.074 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".