The Evaluation of a Web-Based Intervention (Deprexis) to Decrease Depression and Restore Functioning in Veterans: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Depressive symptoms are common in veterans, and the presence of these symptoms increases disability as well as suicidal thoughts and behaviors. However, there is evidence that these symptoms often go untreated. Intervening before symptoms become severe and entrenched is related to better long-term outcomes, including improved functioning and less disease chronicity. Computer-delivered interventions may be especially appropriate for those veterans with mild to moderate depressive symptoms, because these interventions can require fewer resources and have lower barriers to access and thus have potential for wider reach. Despite this potential, there is a dearth of research examining computerized interventions for depressive symptoms in veteran samples. OBJECTIVE: The aim of this study is to evaluate the efficacy of Deprexis (GAIA AG), a computerized intervention for depressive symptoms and related functional impairment. METHODS: Veterans will be recruited through the US Department of Veterans Affairs electronic medical record and through primary care and specialty clinics. First, qualitative interviews will be completed with a small subset of veterans (n=16-20) to assess the acceptability of treatment procedures. Next, veterans (n=132) with mild to moderate depressive symptoms will be randomly assigned to the fully automated Deprexis intervention or a treatment-as-usual control group. The primary outcomes will be self-reported depressive symptoms and various dimensions of psychosocial functioning. RESULTS: This project was funded in May 2024, and data collection will be conducted between October 2024 and April 2029. Overall, 4 participants have been recruited as of the submission of the manuscript, and data analysis is expected in June 2029, with initial results expected in November 2029. CONCLUSIONS: This study will provide initial evidence for the efficacy of self-guided, computerized interventions for depressive symptoms and functional impairment in veterans. If effective, these types of interventions could improve veteran access to low-resource psychosocial treatments. TRIAL REGISTRATION: ClinicalTrials.gov NCT06217198; https://www.clinicaltrials.gov/study/NCT06217198. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/59119.
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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 | 0.035 | 0.030 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.073 | 0.012 |
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".