Alexithymia and emotional regulation: A cluster analytical approach
Bibliographic record
Abstract
BACKGROUND: Alexithymia has been a familiar conception of psychosomatic phenomenon. The aim of this study was to investigate whether there were subtypes of alexithymia associating with different traits of emotional expression and regulation among a group of healthy college students. METHODS: 1788 healthy college students were administered with the Chinese version of the 20-item Toronto Alexithymia Scale (TAS-20) and another set of questionnaires assessing emotion status and regulation. A hierarchical cluster analysis was conducted on the three factor scores of the TAS-20. The cluster solution was cross-validated by the corresponding emotional regulation. RESULTS: The results indicated there were four subtypes of alexithymia, namely extrovert-high alexithymia (EHA), general-high alexithymia (GHA), introvert-high alexithymia (IHA) and non-alexithymia (NA). The GHA was characterized by general high scores on all three factors, the IHA was characterized by high scores on difficulty identifying feelings and difficulty describing feelings but low score on externally oriented cognitive style of thinking, the EHA was characterized by high score on externally oriented cognitive style of thinking but normal score on the others, and the NA got low score on all factors. The GHA and IHA were dominant by suppressive character of emotional regulation and expression with worse emotion status as compared to the EHA and NA. CONCLUSIONS: The current findings suggest there were four subtypes of alexithymia characterized by different emotional regulation manifestations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".