Napping and psychiatric disorders: A systematic review
Bibliographic record
Abstract
Napping is associated with adverse health outcomes in healthy adults. Given limited evidence in psychiatric disorders, this PRISMA-compliant review examined napping across various conditions, including studies on prevalence or clinically relevant outcomes related to disease course. Among the 8275 records identified from PubMed, Web of Science, and Cochrane Library, 40 were included. Risk of bias was assessed using the Newcastle-Ottawa Scale, RoB 2.0, and ROBINS-I tools, by study design. Most studies concerned patients with depressive disorders, who often nap more frequently and longer than controls, with plausible differential impact across subpopulations. In some, like pregnant women, napping may represent a risk factor; in others, a negative prognostic marker. Fewer studies suggest napping benefits mood, well-being and memory. In bipolar disorder, napping appears prevalent and may increase the risk of depressive symptoms. Research on schizophrenia spectrum and anxiety disorders research is contradictory but generally shows higher napping prevalence versus controls, and links napping to increased anxiety risk in elderlies. Conversely, napping does not appear specifically associated with eating or neurodevelopmental disorders, although data on the latter remain scarce in adults. Overall, daytime napping is prevalent in most psychiatric disorders and may represent a risk and/or prognostic factor, deserving systematic clinical 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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".