Assessing Reliability and Validity of Farsi Version of the Toronto Alexithymia Scale-20 in a Sample of Opioid Substance Use Disordered Patients
Bibliographic record
Abstract
Objective: The aim of this study was to investigate the reliability and validityof the Farsi version of the Toronto Alexithymia Scale-20 in a sample of opioidsubstance use disordered patients. Methods: 321 substance dependent patients (287 male, 34 female)participated in this study. All of the participants were asked to complete theFarsi version of the Toronto Alexithymia Scale-20 (FTAS-20), the EmotionalIntelligence Scale (EIS-41), and The Mental Health Inventory (MHI). In orderto examine the internal consistency of the FTAS-20, Cronbach's alphacoefficients were calculated for the entire sample. Pearson's correlationcoefficient was used to estimate the test-retest reliability of the alexithymiadimensions. To examine the concurrent validity of the FTAS-20, a series ofzero-order correlations were conducted between the FTAS-20 subscales,emotional intelligence and mental health variables. Confirmatory FactorAnalysis (CFA) was utilized to test the three-factor structure of the FTAS-20. Results: The internal consistency, test-retest reliability, concurrent validity,and the three-factor structure of the Farsi version of the TAS-20 forsubstance users were supported by findings. Conclusions: The factors found in the Farsi version of the TAS-20, aresimilar to the three factors found in a study conducted by Bagby, Parker andTaylor; the factors were accordingly labeled as Difficulty Identifying Feelings(DIF), Difficulty Describing Feelings (DDF) and Externally-Oriented Thinking(EOT). The results provide evidence for applicability of the TAS-20 and itscross-cultural validity.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".