Relationship between Alexithymia and Sleep Quality in University Students
Bibliographic record
Abstract
Sleep quality is a significant issue for university students because of its impacts on academic performance. It is worth noting that the relationship between alexithymia and sleep quality should be studied. The aim of this study is to investigate the relation between alexithymia and sleep quality in university students. The present study was conducted with 1192 university students. Participants were evaluated using the sociodemographic data form, Toronto Alexithymia Scale-20 (TAS-20) and Pittsburg Sleep Quality Index (PSQI).The obtained data were subjected to statistical analysis. The study included 1192 participants. Of the participants 54% were women. The average age was 21.94±3.31 years. The participants were separated into two groups such as "good sleep quality" and "poor sleep quality," according to their PSQI scores. A statistical difference was found between two groups in terms of TAS-20 with a total of, Difficulty Identifying Feelings, Difficulty Describing Feelings, and Externally-Oriented Thinking. A statistically significant and positive correlation was found between the PQSI and Body Mass Index (BMI), as well as TAS-20 total score. BMI and TAS-20 were revealed to be significant predictors of poor sleep quality. The present study reveals that alexithymia and sleep quality may be related. It is important for future studies to focus on the factors that mediate this relationship.
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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 | 0.000 | 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".