Evaluating the Use of a Computerized CBT Program for Outpatients on a Waitlist in a University CBT Unit
Bibliographic record
Abstract
OBJECTIVES AND DESIGN: The goal of this pilot randomized controlled trial was to determine whether a computerized cognitive-behavioral therapy (cCBT) program for depression and anxiety could reduce symptoms in outpatients on a waitlist for face-to-face CBT for a variety of mental health complaints. METHODS: Sixty-seven outpatients referred for CBT for disparate problems (eg, anxiety, depression, obsessions or compulsions) were randomized to 1 of 2 conditions: (1) the cCBT program "Good Days Ahead," which included weekly guidance and support, or (2) a control condition where patients were referred to a freely available online CBT workbook. Measures of psychological distress were administered at the start of study and at the end of the waiting period, when participants were formally diagnosed and assessed for face-to-face therapy. RESULTS: For the most part, mixed-design analyses of variances revealed no statistically significant changes in symptom measures over time. Nonsignificant interactions and modest effect sizes between groups across time suggest that the cCBT group did not do better than the control group. The majority of cCBT participants reported that the program was "very" or "extremely useful," while only a portion of the control group felt the same about the workbook. There were notable differences in the completion rates of the 2 groups in favor of the cCBT program. CONCLUSIONS: Offering a general cCBT program to waiting list patients may not confer an advantage over referring them to an online workbook, at least in terms of symptom reduction. Results could be partly explained by difficulties translating knowledge into practice, especially if participants' main problem was not directly addressed by the intervention.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".