Bibliographic record
Abstract
Insomnia is a pervasive condition with various causes, manifestations, and health consequences. Regardless of the initial cause or event that precipitates insomnia, it is perpetuated into a chronic condition through learned behaviors and cognitions that foster sleeplessness. This article reviews the rationale and objectives of cognitive-behavioral therapy (CBT), a safe and effective treatment for insomnia that may be used to augment hypnotic drugs or as a monotherapy. Cognitive-behavioral management of insomnia includes 3 components--behavioral, cognitive, and educational modules--and is usually presented in a group or individual therapy setting. Each treatment procedure is detailed herein, and recommendations for implementation are given. The evidence supporting this behavioral approach shows that CBT is effective for 70% to 80% of patients and that it can significantly reduce several measures of insomnia, including sleep-onset latency and wake-after-sleep onset. Aside from the clinically measurable changes, this therapy system enables many patients to regain a feeling of control over their sleep, thereby reducing the emotional distress that sleep disturbances cause. Some clinical and practical issues that often arise when implementing this therapeutic approach for insomnia are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".