Reducing Alcohol Misuse and Promoting Treatment Initiation Among Veterans Through a Brief Internet-Based Intervention: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Young adult veterans who served after the September 11 attacks on the United States in 2001 (ie, post-9/11) are at heightened risk for experiencing behavioral health distress and disorders including hazardous drinking, posttraumatic stress disorder, and depression. These veterans often face significant barriers to behavioral health treatment, and reaching them through brief mobile phone-based interventions may help reduce drinking and promote treatment engagement. OBJECTIVE: Following a successful pilot study, this randomized controlled trial (RCT) aims to further test the efficacy of a brief (ie, single session) mobile phone-delivered personalized normative feedback intervention enhanced with content to promote treatment engagement. METHODS: We will conduct an RCT with 800 post-9/11 young adult veterans (aged 18 to 40 years) with potentially hazardous drinking and who have not recently received treatment for any behavioral health problems. Participants will be randomly assigned to the personalized intervention or a control condition with resources for seeking care. The personalized normative feedback module in the intervention focuses on the correction of misperceived norms of peer alcohol use and uses empirically informed approaches to increase motivation to address alcohol use and co-occurring behavioral health problems. Past 30-day drinking, alcohol-related consequences, and treatment-seeking behaviors will be assessed at baseline and 3, 6, 9, and 12 months post intervention. Sex, barriers to care, posttraumatic stress disorder, depression, and severity of alcohol use disorder symptoms will be explored as potential moderators of outcomes. RESULTS: We expect recruitment to be completed within 6 months, with data collection taking 12 months for each enrolled participant. Analyses will begin within 3 months of the final data collection point (ie, 12 months follow-up). CONCLUSIONS: This RCT will evaluate the efficacy of a novel intervention for non-treatment-seeking veterans who struggle with hazardous drinking and possible co-occurring behavioral health problems. This intervention has the potential to improve veteran health outcomes and overcome significant barriers to treatment. TRIAL REGISTRATION: ClinicalTrials.gov NCT04244461; https://clinicaltrials.gov/study/NCT04244461. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59993.
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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 | 0.033 | 0.031 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.089 | 0.014 |
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".