Summary of the best evidence that cognitive behavioral therapy for insomnia improves sleep quality in patients with chronic insomnia
Bibliographic record
Abstract
Aim To evaluate and summarize the evidence that cognitive behavioral therapy for insomnia improves sleep quality in patients with chronic insomnia and provide reference for clinical practice. Design The PIPOST model from the Center for Evidence-Based Nursing at Fudan University was used to retrieve evidence and integrate it through structured evidence-based questions. Methods Current literatures were systematically searched for the best evidence that cognitive behavioral therapy for insomnia improves sleep quality in patients with chronic insomniaLiterature types included clinical guidelines,best practice information sheets, expert consensuses, systematic reviews, evidence summaries and cohort studies. Data Sources UpToDate,BMJ Best Practice,Joanna Briggs Institute,Guidelines International Network,National Institute for Health and Care Excellence,Registered Nurses Association of Ontario,Scottish Intercollegiate Guidelines Network,the Cochrane Library, Embase,PubMed, Sinomed,Web of Science,DynaMed,MEDLINE, CNKI, WanFang database, Chinese Medical Journal Full-text Database,The search period was from build to December 10, 2024 Results A total of 28 papers were included,including 5 guidelines,3 expert consensus papers,12 systematic evaluations, and 8 Meta-analyses, and the overall quality of the included papers was high.Forty-one pieces of best evidence were summarized in terms of diagnostic criteria for sleep disorders,assessment conditions, timing of initiation of multicomponent cognitive behavioral therapy for sleep (CBT-I), treatment format,composition of components,assessment metrics,assessment tools, symptom improvement metrics, comparisons of implementers, and adverse effects. Conclusion The study summarizes the best evidence that CBT-I improves sleep quality in patients with chronic insomnia and recommends that clinical staff should fully assess the patient’s overall condition before implementing the therapy and develop a personalized CBT-I treatment plan for the patient based on their assessment.
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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 | 0.016 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.016 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".