Heterogeneity of Alexithymia Subgroups
Bibliographic record
Abstract
Abstract: Alexithymia is a subclinical experience in which individuals struggle to identify, distinguish, and describe their own emotions. It is most commonly measured with the self-reported Toronto Alexithymia Scale-20 (TAS-20). However, scholars hold different views on its structure, resulting in challenges in classifying individuals with alexithymia, which is detrimental to clinical diagnosis, counseling, and intervention. The present study aimed to investigate the types (or subgroups) of alexithymia within a sample of college students ( n = 707) from four Chinese universities. Two latent classes of three-factor two-class model solution were effectively identified by the Factor Mixture Model (FMM) approach: a “High-EOT alexithymia” class (18.2%) and a “Non-alexithymia” class (81.8%). The two subgroups exhibited similar performance in difficulty in identifying feelings (DIF) and difficulty in describing feelings (DDF), but they differed significantly in externally oriented thinking (EOT). This suggests that EOT might be a diagnostic criterion 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".