Comparative efficacy of behavior therapy, cognitive therapy, and cognitive behavior therapy for chronic insomnia: A randomized controlled trial.
Bibliographic record
Abstract
OBJECTIVE: To examine the unique contribution of behavior therapy (BT) and cognitive therapy (CT) relative to the full cognitive behavior therapy (CBT) for persistent insomnia. METHOD: Participants were 188 adults (117 women; M age = 47.4 years, SD = 12.6) with persistent insomnia (average of 14.5 years duration). They were randomized to 8 weekly, individual sessions consisting of BT (n = 63), CT (n = 65), or CBT (n = 60). RESULTS: Full CBT was associated with greatest improvements, the improvements associated with BT were faster but not as sustained and the improvements associated with CT were slower and sustained. The proportion of treatment responders was significantly higher in the CBT (67.3%) and BT (67.4%) relative to CT (42.4%) groups at post treatment, while 6 months later CT made significant further gains (62.3%), BT had significant loss (44.4%), and CBT retained its initial response (67.6%). Remission rates followed a similar trajectory, with higher remission rates at post treatment in CBT (57.3%) relative to CT (30.8%), with BT falling in between (39.4%); CT made further gains from post treatment to follow up (30.9% to 51.6%). All 3 therapies produced improvements of daytime functioning at both post treatment and follow up, with few differential changes across groups. CONCLUSIONS: Full CBT is the treatment of choice. Both BT and CT are effective, with a more rapid effect for BT and a delayed action for CT. These different trajectories of changes provide unique insights into the process of behavior change via behavioral versus cognitive routes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| 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 | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".