Computer-based training for cognitive behavioral therapy (CBT4CBT): A mixed methods investigation
Bibliographic record
Abstract
Computer-Based Training for Cognitive Behavioral Therapy (CBT4CBT) is an online intervention for individuals with substance use disorder (SUD). Objective: The aim of this study was: (1) to investigate changes in CBT related skills, quality of life, and SUD severity in adults completing a CBT4CBT intervention, and (2) to explore the participant experience of CBT4CBT in a tertiary hospital specializing in mental health and substance use health in Canada. Methods: Participants included 51 adults seeking treatment for SUD. Participants received access to CBT4CBT over 8 weeks. Measures assessing CBT skills and associated constructs (distress tolerance, change assessment, drug-taking confidence, and change strategies inventory), substance use outcomes, and quality of life were collected at baseline and post-treatment. A qualitative interview was conducted with 26 participants post-treatment. Statistical analysis was conducted using a series of linear mixed effects models examining changes from week 0 to week 8 across all measures. Results: Significant effects of time were found for SUD symptoms, change strategies, distress tolerance, and quality of life. Qualitative analysis found that participants reported the skills modules on managing triggers and dealing with cravings to be the most helpful. Further, participants found the convenience and relatability of the program scenarios most beneficial. Participants suggested that a live connection to a researcher or a practitioner and some technology enhancements would improve the program. Conclusion: The results suggest that CBT4CBT is linked with a reduction in SUD symptoms and an increase in CBT skills, including change strategies and distress tolerance, as well as improved quality of life.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".