A Stage-Based Mobile Intervention for Substance Use Disorders in Primary Care
Bibliographic record
Abstract
Background: Over 20 million American adults meet diagnostic criteria for a substance use disorder (SUD). However, only 10-11% of individuals requiring SUD treatment receive it. Given their access to patients, primary care providers are in a unique position to perform universal screening, brief intervention, and referral to treatment (SBIRT) to fill gaps in services and make referrals to specialty treatment when indicated. While SBIRT has shown success in reducing tobacco and non-dependent alcohol use, the data on SBIRT for dependent alcohol use and illicit drug use are less promising. Major barriers to SBIRT include limited time and resources for SBIRT among providers and low motivation to change among many patients. Objective: The objective of this research was to develop and test the acceptability of a prototype of a mobile-delivered substance use risk intervention (SURI) for primary care patients and a clinical dashboard for providers that can address major barriers to SBIRT for risky drug use. To reduce provider burden, SURI delivers universal screening and feedback on SUD risk via mobile tools to patients at home or in the waiting room. For patients at risk, it also delivers a brief intervention based on the Transtheoretical Model of Change to facilitate progress through the stages of change for quitting the most problematic drug and for seeking treatment if indicated. The prototype also delivers 30 days of stage-matched text messages and four online activities addressing key topics (eg, managing cravings). For providers, the clinical dashboard summarizes the patient’s SUD risk scores and stage of change data and provides stage-matched scripts to guide in-person sessions. Methods: Feasibility test participants were 3 providers at a federally qualified health center and 5 of their patients with a known SUD. Providers completed a 45-minute webinar training session on the SURI tools, delivered dashboard-guided SBIRT session(s), and completed a brief acceptability survey. Patients completed an online SURI session and in-person SBIRT session, accessed other program components, and completed 3 acceptability surveys over 30 days. Questions in the surveys were adapted from the National Cancer Institute’s Education Materials Review Form. Response options ranged from 1=strongly disagree to 5=strongly agree. The criterion for establishing feasibility was an overall rating of 4 or higher across items. Results: For providers, mean acceptability ratings ranged from 3.7 to 5.0, with an overall mean rating of 4.3. Notably, all providers gave a rating of 5.0 for the item “The program can give me helpful information about my patient.” One patient dropped out of the study before accessing any study materials. For the remaining patients, mean acceptability ratings for the mobile- and provider-delivered SBIRT session ranged from 4.0 to 5.0, with an overall mean rating of 4.5; mean ratings of the follow-up text messages and online activities ranged from 3.6 to 4.8, with an overall rating of 4.0. One of the most highly rated items was “The program could help me make some positive changes” (4.5). Conclusions: The SURI program and clinical dashboard, developed to reduce barriers to SBIRT in primary care, were well received by care providers and patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".