Increasing Peer Support for Opioid Use Disorder Recovery During COVID-19 Through Digital Health: Protocol for a National Randomized Controlled Trial
Bibliographic record
Abstract
Background Increasing numbers of opioid overdoses have been observed during the COVID-19 pandemic, likely reflecting the pandemic’s multiple effects on this already vulnerable population. People in recovery from opioid use disorder (OUD) have reported disproportionate psychosocial distress and isolation, as well as significant disruptions in access to treatment, including peer support, during the COVID-19 pandemic. Peer support is a key component of many evidence-based OUD recovery programs; it improves recovery capital, treatment engagement, and perceived social support and reduces psychosocial distress, particularly when used in conjunction with other evidence-based treatments, such as medication for OUD. Objective This study aims to evaluate a novel mobile peer support app platform among a national sample of individuals in recovery from OUD as an adjunct to usual care during the COVID-19 pandemic. Methods Individuals residing in the United States who are aged ≥18 years; own a smartphone; and self-report being in recovery for an OUD, being in treatment for an OUD (ie, in the past 30 days received prescribed methadone, naltrexone, or buprenorphine), or currently receiving some form of assisted recovery support (n=1300) will be recruited through online, targeted social media advertisements. Eligible participants will be randomly assigned (1:1) to a mobile peer recovery support intervention utilizing a novel smartphone-based app or to a control. Participants will complete 1 baseline survey and then a follow-up survey 1, 3, and 6 months after randomization. The primary aim of recovery capital will be determined by the change in recovery capital between study groups over the 6-month study period. We will also examine treatment engagement by using administrative data from a subset of individuals (n=650) residing in Rhode Island and Indiana. Results As of June 2022, we enrolled 43 participants. Conclusions If this mobile app demonstrates efficacy among a large national sample of patients, it has the potential to augment existing treatment programs, improve recovery capital, and reduce the disproportionate impacts of COVID-19 on this vulnerable population. Conflicts of Interest None declared. Trial Registration ClinicalTrials.gov NCT05405712; https://clinicaltrials.gov/ct2/show/NCT05405712
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".