Bibliographic record
Abstract
Abstract There is extensive evidence showing that sleep and health are related. Among all of the sleep disorders, insomnia is the most prevalent and one of the most frequent health complaints brought to the attention of health‐care practitioners. A variety of environmental, psychosocial, and biological variables have been linked to insomnia complaints. Insomnia is also associated with significant consequences in health, quality of life, social and occupational functioning, economics, and public safety. The multidimensional nature of insomnia has thus led to the development of various evaluative and diagnostic tools as well as diverse pharmacological and psychological treatment options. This chapter offers an overview of insomnia, providing clues as to its etiology, impact, and clinical presentation and management. We begin with some basic facts about sleep, followed by a description of the nature, diagnosis, epidemiology, and risk factors associated with insomnia. The relationship between sleep and health, mental as well as physical, is then discussed, and a thorough description of assessment methods for the clinical management of insomnia is provided. Finally, we present a review of empirically validated psychological, pharmacological, and combined therapies for insomnia, and underline their benefits and limitations.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.091 | 0.038 |
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".