Further Support for the Psychometric Properties of the Farsi Version of Perth Alexithymia Questionnaire
Bibliographic record
Abstract
Alexithymia is defined as the lack of words to describe emotions and is associated with different psychopathologies. Various tools have been developed for measuring alexithymia; each has its limitations. A new questionnaire, Perth Alexithymia Questionnaire (PAQ), was developed to simultaneously assess positive and negative dimensions. Validation of such a tool in different cultures allows cross-cultural health psychology studies and facilitates knowledge transfer in the field. We aimed to examine the psychometric features of the PAQ in the Farsi-speaking population in Iran. Four-hundred-twenty-nine university students were asked to complete the PAQ, the Toronto Alexithymia Scale (TAS-20), Beck Depression Inventory (BDI-II), Beck Anxiety Inventory (BAI), and emotion regulation questionnaire (ERQ). Concurrent validity, discriminant validity, internal consistency, and test-retest reliability and factor structure were investigated. Confirmatory factor analysis showed a five-factor model identical to the original questionnaire. The questionnaire indicated good internal consistency (0.82 < α < 0.94). Test-retest reliability was acceptable for all subscales. The correlations between PAQ and its subscales with BDI-II, BAI, and TAS, and expression suppression subscale of ERQ were strong for concurrent validity. Concerning the discriminant validity, PAQ and its subscales were not correlated with reappraisal subscales of ERQ. The present findings suggest that the Farsi version of PAQ has strong psychometric properties and is appropriate for use in the Farsi-speaking population.
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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 | 0.006 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".