Psychometric Properties and Configural Invariance of the Polish – Language Version of the 20-Item Toronto Alexithymia Scale in Non-clinical and Alcohol Addict Persons
Bibliographic record
Abstract
AIM: The development and assessment of the psychometric properties of the Polish-language version of the Toronto Alexithymia Scale (Bagby et al., 1994a, b) is described in this article. The aim of this study was to translate the TAS - 20 into Polish and establish the psychometric properties of this instrument evaluating alexithymia. MATERIALS AND METHODS: Data were collected via self-report measures from a total sample of 676 participants: a total of 180 participants (115 males and 65 females) diagnosed with alcohol dependence, and 496 control group (347 males and 149 females). RESULTS: Confirmatory factor analyses found the factor structure of the original English-language TAS 20 for the three subscales translated into Polish: Difficulty in Identifying Feeling (DIF); Difficulty in Describing Feeling (DDF); Externally Oriented Thinking (EOT). All three subscales showed good internal consistency in non-clinical group and two subscales, DIF and DDF in alcohol addict group. Several EOT items loaded poorly on their intended factor. CONCLUSION: The results from the present study indicate that the Polish version of the TAS - 20 is a reliable and valid measure of alexithymia with good levels of internal consistency, homogeneity, and construct validity. We conclude that the TAS-20 has, for the most part, adequate psychometric properties, though interpretation should focus only on the total scale score and DIF and DDF subscales, especially in clinical groups.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".