The Assessment of Alexithymia Across Positive and Negative Emotions: The Psychometric Properties of the Iranian Version of the Perth Alexithymia Questionnaire
Bibliographic record
Abstract
Background: Construct of Alexithymia is important for understanding psychopathology that its assessment is of high interest as persons with difficulty in processing their emotions (either positive and negative) are more vulnerable to psychopathology problems. Objectives: The current study aimed to determine the psychometric properties of the Perth Alexithymia questionnaire (PAQ), and to describe appropriate measures for the field of clinical psychology and psychiatry. Methods: The Persian version of the PAQ was produced through forward translation, reconciliation, and back translation. The study population was all staff (soldiers) of the army force in Tehran, Iran, in 2018 - 2019. Two hundred and fifty four soldiers were selected by convenience sampling method. The following questionnaires were used to collect data: the PAQ, The Deliberate Self-Harm inventory (DSHI), Borderline Personality scale (STB), Cognitive Flexibility inventory (CFI), and Self-Compassion scale (SCS) short-form. The construct validity of the PAQ was evaluated using confirmatory factor analysis, divergent, and convergent validity. Internal Consistency and test-retest reliability (2 weeks’ interval) were applied to evaluate reliability. Data were analyzed using LISREL (version 8.8) and SSPS (version 22). Results: PAQ and its subscales were found as valid and reliable measures, with good internal consistency and good test-retest reliability. The PAQ showed good internal consistency (Cronbach’s α = 0.91). Concerning the convergent validity, PAQ and its subscales showed a significant positive correlation with self-report measures of DSHI and STB (P < 0.05). However, they were negatively correlated with Self-Compassion scale (SCS) short-form and CFI (P < 0.05), which demonstrated a good divergent validity. Moreover, while the results of this study support the five-factor models of the PAQ (RMSEA = 0.08, NFI = 0.94, CFI= 0.95), the two-factor model does not fit the data. Conclusions: The PAQ showed good validity and reliability and can be useful for evaluating Alexithymia in the army force samples. The PAQ can be considered promising as a measure in Alexithymia-related research and clinical settings.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".