Bibliographic record
Abstract
Sleep disorders are frequently associated with mental disorders. Largely underdiagnosed in psychiatry, sleep disorders can alter the health of patients : mental health, with increased severity of the mental disorder, more frequent relapses, greater therapeutic resistance, increased risk of suicide ; and physical health, with particular cardiovascular and metabolic complications. To facilitate the diagnosis of sleep disorders in psychiatry, it is essential to be able to identify them early, in particular using simple, rapid and widely used tools such as self-questionnaires. This work provides a review of the self-questionnaires used to screen for sleep disorders. The so-called "generic" questionnaires, allowing the detection of several sleep disorders with a single questionnaire, and the so-called "specific" questionnaires of a disorder, allowing the detection of a single sleep disorder in particular, will be presented. This review shows that over the historical development of the various questionnaires, sleep physicians were first interested in screening for sleep disturbances, then in screening sleep disorders and more recently, in a broader and more ambitious concept, that of sleep health. Knowledge of the screening tools for sleep disorders in psychiatry prompts us not to simply ensure the absence of sleep disorders, but also to promote well sleep health in patients with mental disorders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.989 | 0.989 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".