Computerized Cognitive Behavioral Therapy Intervention for Depression Among Veterans: Acceptability and Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Computerized cognitive behavioral therapies (cCBTs) have been developed to deliver efficient, evidence-based treatment for depression and other mental health conditions. Beating the Blues (BtB) is one of the most empirically supported cCBTs for depression. The previous trial of BtB with veterans included regular guidance by health care personnel, which increased the complexity and cost of the intervention. OBJECTIVE: This study, conducted by researchers at a Veterans Affairs Medical Center, aims to test the acceptability and feasibility of unguided cCBT for depression among US military veterans. METHODS: To examine the acceptability of BtB delivered without additional peer or other mental health care provider support, a before-and-after trial was conducted among United States (US) military veterans experiencing mild to moderate depressive symptoms. The feasibility of the study design for a future efficacy trial was also evaluated. RESULTS: In total, 49 veterans completed preintervention assessments and received access to BtB, and 29 participants completed all postintervention assessments. The predetermined acceptability criterion for the intervention was met. Although the predetermined feasibility criteria regarding screening eligibility rate, number of BtB modules completed, and completion of a posttreatment assessment were not met, the results were comparable with those of other cCBT studies. CONCLUSIONS: This is the first study among US military veterans to demonstrate support for the implementation of cCBT for depression without the assistance of a mental health professional or a peer support specialist, suggesting that stand-alone computer-aided interventions may be viable. Ideas for improving feasibility in future trials based on this study are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".