Assessing Alexithymia: Psychometric Properties of the Perth Alexithymia Questionnaire in a Spanish-Speaking Sample
Bibliographic record
Abstract
Alexithymia is a trait composed of difficulties identifying feelings (DIF), difficulties describing feelings (DDF), and externally orientated thinking (EOT). It is an important transdiagnostic risk factor for psychosomatic disorders and other types of emotion-based psychopathologies, and can reduce the efficacy of some treatment approaches. Alexithymia assessments are therefore important in psychiatric and research settings. The Perth Alexithymia Questionnaire (PAQ) was recently developed to enable more comprehensive alexithymia assessments, however, its psychometric properties need further examination and it is so far only available in English. In this study, we sought to address this by translating the PAQ into Spanish and testing its psychometric properties in an adult sample from Chile (N = 370). Confirmatory factor analyses found the PAQ to have a theoretically congruent factor structure, supporting the contemporary status of alexithymia as a multifaceted construct and the PAQ's capacity to assess the DIF, DDF, and EOT facets of alexithymia across negative and positive emotions. All subscale and composite scores had high internal consistency reliability, and demonstrated good concurrent and discriminant validity. The PAQ therefore appears to provide a robust and detailed alexithymia profile. This Spanish version should help to enable more comprehensive cross-cultural research into alexithymia and its role in and psychological disorders.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".