An Automated Text Messaging Intervention to Reduce Substance Use Self-Stigma (Project RESTART): Protocol for a Feasibility and Acceptability Pilot Study
Bibliographic record
Abstract
BACKGROUND: Stigma is a barrier to treatment and harm reduction seeking in people who use drugs. Most stigma reduction interventions offer psychotherapy or psychoeducation in group-based clinical settings, failing to reach people who are not in treatment. SMS text messaging is an effective and acceptable modality for delivering health information to people who use drugs and may be a suitable conduit for providing information and advice to understand and cope with stigma. OBJECTIVE: This paper presents the protocol for a study that aims to determine the feasibility, acceptability, and preliminary effectiveness of a 4-week automated SMS text message intervention to increase stigma resistance and reduce self-stigma in people who use drugs. METHODS: We designed a novel automated SMS text message intervention to address the four personal-level constructs of stigma resistance: (1) not believing stigma and catching and challenging stigmatizing thoughts, (2) empowering oneself through learning about substance use and one's recovery, (3) maintaining one's recovery and proving stigma wrong, and (4) developing a meaningful identity and purpose apart from one's substance use. Theory-based messages were developed and pilot-tested in qualitative elicitation interviews with 22 people who use drugs, resulting in a library of 56 messages. In a single-group, within-subjects, community-based pilot trial, we will enroll 30 participants in the Resisting Stigma and Revaluating Your Thoughts (RESTART) intervention. Participants will receive 2 daily SMS text messages for 4 weeks. Implementation feasibility will be assessed through recruitment, enrollment, retention, and message delivery statistics. User feasibility and acceptability will be assessed at follow-up using 23 survey items informed by the Theoretical Framework of Acceptability. Primary effectiveness outcomes are changes in self-stigma (Substance Abuse Self-Stigma Scale) and stigma resistance (Stigma Resistance Scale) from baseline to follow-up measured via a self-administered survey. Secondary outcomes are changes in hope (Adult Dispositional Hope Scale) and self-esteem (Rosenberg Self-Esteem Scale). Feasibility and acceptability will be assessed with descriptive statistics; effectiveness outcomes will be assessed with paired 2-tailed t tests, and group differences will be explored using ANOVA. Overall, 12 participants will also be selected to complete acceptability interviews. RESULTS: This pilot study was funded by the National Institute on Drug Abuse in April 2023 and received regulatory approval in January 2024 by the University of North Carolina-Chapel Hill Institutional Review Board. Recruitment and enrollment began in March 2024. Follow-up visits are expected to conclude by May 2024. Results will be disseminated in relevant peer-reviewed journals. CONCLUSIONS: To the best of our knowledge, this is the first study to address substance use stigma via a self-help SMS text messaging program. Results will add to the nascent literature on stigma reduction in people who use drugs. This protocol may interest researchers who are considering text messaging to address psychosocial needs in hard-to-reach populations. TRIAL REGISTRATION: ClinicalTrials.gov NCT06281548; https://clinicaltrials.gov/ct2/show/NCT06281548. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59224.
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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 | 0.021 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.052 | 0.007 |
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".