The Impact of Alexithymia on Quality of Life in a Sample of Healthy University Students: A Structural Equation Modeling Approach
Bibliographic record
Abstract
The relationship between alexithymia and quality of life has attracted the attention of researchers recently. However, these studies have been conducted on patient groups or the general population. This study aims to determine the simultaneous effect of the level of alexithymia on the components of quality of life in a healthy university student population using the structural equation model. The sample of this cross-sectional study consists of 183 healthy volunteer university students without mental and physical illnesses. Sociodemographic Data Form, Short Form-36 (SF-36) Quality of Life Scale, and Toronto Alexithymia Scale (TAS-20) were applied to the participants. In this current study, 48.6% (n = 89) of the participants were female and 51.4% (n = 94) were male, and the mean age was 21.88 ± 2.11 years. 13.1% (n = 24) of the volunteers were categorized as alexithymic, 25.2% (n = 46) as borderline alexithymic and 61.7% (n = 113) as non-alexithymic. It has been shown that Toronto Alexithymia Scale has an inverse significant and moderate effect on the SF-36 Quality of Life Scale (Standardized regression coefficient -0.40). Our study shows that alexithymia has a detrimental impact on the quality of life of healthy university students who are not suffering from any medical or mental illnesses. It is thought that it would be beneficial to develop specific intervention methods for alexithymic individuals to remove these alexithymia-related problems and improve quality of life. Longitudinal research in the future will be beneficial in explaining the causal relationships between alexithymia and quality of life.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".