Psychometric properties of the Toronto Alexithymia Scale (TAS-20) in Brazil
Bibliographic record
Abstract
Internationally, the most widely used self-report instrument to assess alexithymia (a clinical condition characterized by serious deficits in the cognitive processing of emotions) is the Toronto Alexithymia Scale (TAS-20). Because this measure has been poorly investigated in Brazil and emerging research suggests that TAS-20 scores might vary from one culture to another, we sought to: (1) investigate the psychometric properties of a Brazilian TAS-20; and (2) examine the degree to which Brazilian non-clinical TAS-20 scores differ from the scores of non-clinical samples from other cultures. A sample of 850 non-clinical Brazilian adults were administered a number of questionnaires and performance-based measures via online data collection. Data analyses inspected internal consistency and factor structure of the TAS-20, and tested the association of TAS-20 scores to emotional functioning and psychopathology. In line with previous international research, the Brazilian TAS-20 showed acceptable to adequate psychometric properties. Furthermore, TAS-20 scores associated negatively with empathy and emotional perception, and positively with emotion dysregulation and personality traits like dependency, abrupt changes in mood, and avoidance of criticism. Also noteworthy, our non-clinical Brazilian TAS-20 scores were very similar to those observed in other previously published non-clinical TAS-20 scores from seven non-Brazilian cultures.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".