Personality traits and alexithymia: A closer look at university students in a cross-sectional study
Bibliographic record
Abstract
This study investigates the association between student personality traits and alexithymia among university students. In this cross-sectional study, 290 students were selected through multistage cluster sampling. Standard questionnaires of 5 personality traits (NEO Five-Factor Inventory) and the Toronto Alexithymia Scale were used to determine alexithymia and personality traits. An independent t test in SPSS 21.0 was performed to compare the scores of several personality traits between students with and without alexithymia. Also, the logistic regression model was used to investigate the adjusted associations. The mean age of participants was 22.6 ± 3.8 years. Most students were female (77.9%) and single (88.5%). According to the Toronto Alexithymia Scale, 127 of 290 students (43.8%) displayed signs of alexithymia. The neuroticism trait was considerably greater in alexithymic students than the others (27.20 ± 3.90 vs 25.48 ± 4.90; P < .01), as well as the conscientiousness trait (20.16 ± 3.84 vs 21.58 ± 5.00; P = .01). Logistic regression showed that each unit increase in neuroticism trait increases alexithymia odds by 10%, while each unit raises in conscientiousness trait decreases odds by 8%. Considering a significant association between personality traits and alexithymia, better screening and interventional programs through personality traits will prevent or alleviate the symptoms of alexithymia among university students.
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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 | 0.002 |
| 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) | 0.001 | 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".