Is Alexithymia Associated With Sleep Disturbances, Independent of Depression and Anxiety?
Bibliographic record
Abstract
Objectives: We aimed to examine the relationship between alexithymia and sleep, independent of depression and anxiety.Methods: This cross-sectional study included participants who visited our sleep clinic between 2016 and 2022. In total, 142 participants (98 males and 44 females) were included, and they completed the Toronto Alexithymia Scale-20 (TAS-20). Epworth Sleepiness Scale (ESS), Pittsburgh Sleep Quality Index (PSQI), and Insomnia Severity Index (ISI) scores were also recorded. The relationship between alexithymia and sleep (PSQI, ISI, and ESS scores) was determined after controlling for demographic and psychological (Beck Depression Inventory and State Trait Anxiety Inventory-Trait) variables.Results: Individuals with alexithymia had significantly higher PSQI, ESS, and ISI scores than those without alexithymia. Correlation analysis showed significant correlations between the total TAS-20 score and ISI (r=0.321) and ESS (r=0.253) scores. In multivariate linear regression analysis, the total TAS-20 score (β=0.201; p=0.022) was significantly associated with the ESS score.Conclusions: Alexithymia was associated with the severity of insomnia and daytime sleepiness in adults. However, when considering depression and anxiety in multivariate analysis, alexithymia was significantly associated with daytime sleepiness; however, its relationship with the severity of insomnia was not significant.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".