Exploring Factor Validity of 20-Item Toronto Alexithymia Scale (TAS-20) in Albanian Clinical and Nonclinical Samples
Bibliographic record
Abstract
This study aims to examine the factor structure and validity of the Albanian TAS-20 (Toronto Alexithymia Scale) using a sample comprised of 342 students and 196 patients from a psychiatric clinic. Based on a literature review of studies of confirmatory factor analysis (CFA), three types of models were tested: first-order models with method factors and covariances, a second-order model with method factors and covariances, and nested models with method factors and covariances. The findings suggest that a three-factor correlated model with method factors was the best and most parsimonious solution for the clinical sample, exhibiting adequate levels of performance based on the goodness of fit criteria. However, regarding the student sample, the nested three-factor model with method factors and covariances demonstrated a superior fit when compared with the other tested models. Although the total scale of difficulty identifying feelings (DIF) and difficulty describing feelings (DDF) scores provided sound internal consistency, the externally oriented thinking (EOT) subscale did not. Nonetheless, as the CFA suggests the plausibility of negatively keyed items in the method factors, further interpretation of this scale is suggested. This study concludes that the TAS-20 of the Albanian language is appropriate for research purposes, and further research is needed for its application for clinical practice.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".