Development of a Revised Urdu Version of the 20-Item Toronto Alexithymia Scale
Bibliographic record
Abstract
Abstract: Introduction: The Toronto Alexithymia Scale (TAS-20) is the most widely used instrument to assess alexithymia and has recently been translated into Urdu. There are several shortcomings with this translation (e.g., removal of four items from the original instrument, grammatical errors, poor/complex item translation) that compromise the assessment of alexithymia for Urdu-speaking persons. In this study, we report the development of a revised Urdu translation of the TAS-20 (TAS-20-UR). Methods: All 20 items of the original TAS-20 were translated into Urdu using a back-translation method, administered to participants from Pakistan ( N = 524), and subjected to psychometric analyses. Confirmatory factor analysis (CFA) was conducted to examine the factor structure of the TAS-20-UR. We also examined the measurement invariance of the scale across Pakistani men and women as well as Pakistani and Canadian community adults using multigroup CFA (MGCFA). Results: The internal reliability was adequate. The three-factor model, which has been recovered in most translations of the scale, produced an adequate-to-good fit. MGCFA supported strict invariance across Pakistani men and women, and partial scalar invariance across Pakistani and Canadian community adults. Limitations: Further research is required to confirm the validity of the TAS-20-UR. Also, the findings are only generalizable to literate individuals in Pakistan since data was not collected from non-Urdu readers. Discussion: The TAS-20-UR is reliable and captures the alexithymia construct; we recommend it for use in research settings in which Urdu is spoken.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".