Nature and Treatment of Insomnia
Bibliographic record
Abstract
There is a strong association between sleep and health. Both mental and physical health are very much dependent on adequate sleep quality and duration; likewise, healthy sleep is much dependent on good physical and mental health. Not surprisingly, there is a very high rate of comorbidity between sleep disturbances and mental and physical health problems. Insomnia is the most common of all sleep disorders, affecting nearly 25% of all adults at least occasionally and 10% on a more persistent basis. Chronic insomnia produces negative consequences on numerous aspects of quality of life and is a risk factor for mental (e.g., depression) and physical (e.g., hypertension) health problems. After presenting an overview of some basic facts about sleep and the impact of sleep loss on different areas of functioning, this chapter reviews the nature and treatment of insomnia. The nature of insomnia complaints and its epidemiology is summarized, with a summary of the evidence on its natural history, prevalence, risk factors, and long-term course. This is followed by a description of validated assessment methods for sleep/wake complaints and a review of current therapeutic options for the management of insomnia, with a predominant emphasis on cognitive-behavioral approaches.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".