Identifying the active ingredients of a behavioral activation-based digital single-session intervention
Bibliographic record
Abstract
Digital single-session interventions (SSIs) have been shown to be effective in reducing myriad mental health conditions. However, it is unclear which components of SSIs drive their therapeutic effects. In this study, we divided a well-evaluated behavioral activation-based SSI into three candidate components—PSYCHOEDUCATION, TESTIMONIALS, and ACTION PLAN—each hypothesized to have independent therapeutic value. We conducted a 23 factorial experiment (N=889) to evaluate effects of the individual components on depressive symptoms. Our results showed that only the ACTION PLAN candidate component had a significant effect on depressive symptoms at 2-week (d=−0.18) and 8-week (d=−0.12) follow-ups. Additionally, user’s perceptions of the intervention’s credibility and their expectations of improvement were significantly associated with a reduction in depressive symptoms at both time points. Our findings offer a more nuanced understanding of which elements within digital SSIs may drive their therapeutic benefits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".