Examining the Incremental Validity of the Perth Alexithymia Questionnaire (PAQ) Relative to the 20-Item Toronto Alexithymia Scale (TAS-20)
Bibliographic record
Abstract
The 20-item Toronto Alexithymia Scale (TAS-20) is the most widely used instrument for assessing alexithymia, with more than 25 years of research supporting its reliability and validity. The items that compose this scale were written to operationalize the components of the construct that are based on clinical observations of patients and thought to reflect deficits in the cognitive processing of emotions. The Perth Alexithymia Questionnaire (PAQ) is a recently introduced measure and is based on a theoretical attention-appraisal model of alexithymia. An important step with any newly developed measure is to evaluate whether it demonstrates incremental validity over existing measures. In this study using a community sample (N = 759), a series of hierarchical regression analyses were conducted that included an array of measures assessing constructs closely associated with alexithymia. Overall, the TAS-20 showed strong associations with these various constructs to which the PAQ was unable to add any meaningful increase in prediction relative to the TAS-20. We conclude that until future studies with clinical samples using several different criterion variables demonstrate incremental validity of the PAQ, the TAS-20 should remain the self-report measure of choice for clinicians and researchers assessing alexithymia, albeit as part of a multi-method approach.
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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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".