The Prediction of Alexithymia Using Depression, Anxiety, Stress, and Demographics in Undergraduate Students
Bibliographic record
Abstract
Introduction: Alexithymia is a psychiatric disorder in which people become emotionally frustrated. This study aims to model the role of depression, anxiety, and stress in alexithymia prediction. Methods: In this cross-sectional study, 260 undergraduate students were selected via multi-stage cluster sampling. The Toronto Alexithymia Scale (TAS-20) and depression, anxiety and stress scale have been used to collect data. The association between qualitative variables was examined using Chi-square test and LASSO logistic regression was fitted for alexithymia prediction. Results: The mean±SD of participants’ age was 20.7±3.2 years. Of all, 197 (75.8%) students were female and 236 (90.8%) were single. According to the cutoff point for TAS-20, 30.8% of the students displayed signs of alexithymia. The rate of alexithymia was significantly higher among males (42.9% versus 26.9%, P=0.02) and among nursing (45.9%) and anesthesia (44.8%) students than other undergraduate students. The proportion of students with anxiety, depression, and stress were 45.0%, 15.8%, and 9.2%, respectively. 51.2% of the depressed students had alexithymia, while only 26.9% of non-depressed students were alexithymic (P=0.002). LASSO logistic regression showed that odds of alexithymia was significantly higher among male students (OR=1.40, 95% CI=1.03, 1.90), students with depression (OR=1.73, 95% CI=1.18, 2.54), students who had anxiety (OR=1.42, 95% CI=1.07, 1.89), and nursing students (OR=1.62, 95% CI=1.07, 2.45). Conclusion: The results of this study indicate the importance role of anxiety and depression in predicting alexithymia. Due to the high prevalence of alexithymia among college students, we suggest the routine evaluation of college students for alexithymia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".