Sequential Treatment for Chronic Insomnia: A Pilot Study
Bibliographic record
Abstract
This article explores the efficacy of sequential treatment involving medication and cognitive behavioral treatment (CBT) for insomnia. In a multiple baseline across-subjects design, 6 participants with primary chronic insomnia received 1 of the following treatment sequences: (a) concurrent combination of medication and CBT for the 10-week treatment duration (Combined); (b) medication for the first 5 weeks, with introduction of CBT at week 4 and medication withdrawal after the 5th week resulting in treatment overlap during weeks 4 and 5 (Overlapping: Medication --> Combined --> CBT); and (c) medication alone for the first 5 weeks followed by CBT alone for an additional 5 weeks (Medication --> CBT). Each sequence led to significant sleep improvements, but these improvements occurred at different times during the intervention. Participants in the Combined and in the Overlapping sequences improved their sleep during the 1st phase of treatment, whereas those in the Medication --> CBT sequence improved mostly during the 2nd phase of treatment. These preliminary results suggest that a sequential treatment is effective for chronic insomnia. In addition, the results suggest that sleep improvements are more likely to emerge when CBT is introduced, with an Overlapping sequence showing a slight advantage over the other sequences. Additional clinical trials should be conducted with larger samples to replicate these preliminary findings.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".