Alexithymia profiles and depression, anxiety, and stress
Bibliographic record
Abstract
BACKGROUND: Alexithymia is a multidimensional trait comprised of difficulties identifying feelings, difficulties describing feelings, and externally orientated thinking. It is regarded as an important risk factor for emotional disorders, but there are presently limited data on each specific facet of alexithymia, or the extent to which deficits in processing negative emotions, positive emotions, or both, are important. In this study, we address these gaps by using the Perth Alexithymia Questionnaire (PAQ) to comprehensively examine the relationships between alexithymia and depression, anxiety, and stress symptoms. METHODS: University students (N = 1250) completed the PAQ and the Depression Anxiety Stress Scales-21. Pearson correlations, hierarchical regressions, and latent profile analysis were conducted. RESULTS: All facets of alexithymia, across both valence domains, were significantly correlated with depression, anxiety, and stress symptoms (r = 0.27-0.40). Regression analyses indicated that the alexithymia facets, together, could account for a significant 14.6 %-16.4 % of the variance in depression, anxiety, and stress. Difficulties identifying negative feelings and difficulties identifying positive feelings were the strongest unique predictors across all symptom categories. Our latent profile analysis extracted eight profiles, comprising different combinations of alexithymia facets and psychopathology symptoms, collectively highlighting the transdiagnostic relevance of alexithymia facets. LIMITATIONS: Our study involved a student sample, and further work in clinical samples will be beneficial. CONCLUSIONS: Our data indicate that all facets of alexithymia, across both valence domains, are relevant for understanding depression, anxiety, and stress. These findings demonstrate the value of facet-level and valence-specific alexithymia assessments, informing more comprehensive understanding and more targeted treatments of emotional disorder symptoms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".