Effects of a Multi-Component Behavioral Intervention (MCI) for Insomnia on Depressive and Insomnia Symptoms in Individuals with High and Low Depression
Bibliographic record
Abstract
Insomnia and depression are prevalent and co-occurring conditions that are associated with significant impairment of life. Previous research indicates that cognitive-behavioral interventions for insomnia (CBT-I) can improve both insomnia and depressive symptoms. The aim of the authors in this study was to determine whether a multi-component behavioral intervention (MCI) improved both insomnia and depressive symptoms in persons presenting with insomnia and high levels of depression. The sample consisted of 321 individuals with insomnia who participated in a trial of insomnia treatments; 106 participants had high levels of depression (score ≥ 16 on CES-D) at baseline. Participants either received the MCI or a control treatment (sleep education and hygiene booklet). At post-test, participants with high and low levels of depressive symptoms showed significant improvement in insomnia symptoms. Those with high depression also had significant reductions in depressive symptoms. It can be concluded that for individuals with depression and insomnia, CBT-I is a viable intervention for managing depressive symptoms, which complements other approaches for treating depression.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".