Electronic Problem-Solving Treatment: Description and Pilot Study of an Interactive Media Treatment for Depression
Bibliographic record
Abstract
BACKGROUND: Computer-automated depression interventions rely heavily on users reading text to receive the intervention. However, text-delivered interventions place a burden on persons with depression and convey only verbal content. OBJECTIVE: The primary aim of this project was to develop a computer-automated treatment for depression that is delivered via interactive media technology. By using branching video and audio, the program simulates the experience of being in therapy with a master clinician who provides six sessions of problem-solving therapy. A secondary objective was to conduct a pilot study of the program's usability, acceptability, and credibility, and to obtain an initial estimate of its efficacy. METHODS: The program was produced in a professional multimedia production facility and incorporates video, audio, graphics, animation, and text. Failure analyses of patient data are conducted across sessions and across problems to identify ways to help the user improve his or her problem solving. A pilot study was conducted with persons who had minor depression. An experimental group (n = 7) used the program while a waitlist control group (n = 7) was provided with no treatment for 6 weeks. RESULTS: All of the experimental group participants completed the trial, whereas 1 from the control was lost to follow-up. Experimental group participants rated the program high on usability, acceptability, and credibility. The study was not powered to detect clinical improvement, although these pilot data are encouraging. CONCLUSIONS: Although the study was not powered to detect treatment effects, participants did find the program highly usable, acceptable, and credible. This suggests that the highly interactive and immersive nature of the program is beneficial. Further clinical trials are warranted. TRIAL REGISTRATION: ClinicalTrials.gov NCT00906581; http://clinicaltrials.gov/ct2/show/NCT00906581 (Archived by WebCite at http://www.webcitation.org/6A5Ni5HUp).
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".