Psychometric characteristics of Russian Language Version of the Bermond–Vorst Alexithymia Questionnaire (BVAQ)
Bibliographic record
Abstract
Relevance and subject of the study. Measuring alexithymia levels is an important task due to the connection between its feature and a range of mental health and behavioral disorders. There is a lack of psychological instruments in the Russian language to investigate the various components of alexithymia. This study examines the psychometric properties of the Russian version of the Bermond-Vorst Alexithymia Questionnaire (BVAQ). Research methods and materials. The total sample consisted of 573 people ages 15-59, 137 males and 446 females. Exploratory and confirmatory analyses were conducted to verify the questionnaire's structure, consistency, and reliability. To test construct validity, we used the Toronto Alexithimia Scale (TAS-20) adapted by E. G. Starostina et al., the Satisfaction With Life Scale of E. Diener adapted by E. N. Osin & D. A. Leontiev, the Marlowe-Crowne Social Desirability Scale adapted by Yu. L. Khanin. Descriptive statistics of the inventory were calculated. Results. Analysis of the factor structure of the questionnaire revealed the greatest suitability of a bifactor model with a common grouping factor and associated subfactors. Models proposing the presence of cognitive and affective factors of alexithymia did not demonstrate adequate suitability levels. Exploratory factor analysis revealed that, while the overall structure of the questionnaire was preserved, the scale "emotionalizing" was divided into two subscales containing direct and inverse points, which may be due to cultural perceptual features. The questionnaire as a whole and its individual scales were characterized by sufficient internal consistency. A satisfactory retest reliability of the method was demonstrated. As part of the convergent validity verification of the inventory, the expected direct correlation between its indicator and the level of alexithymia on the TAS-20 was revealed, as well as a negative correlation with the satisfaction with life indicator. Divergent validity was supported by a lack of significant correlations with social desirability scale score. In general, it can be concluded that the psychometric properties of the proposed version of the BVAQ questionnaire were quite good, but the structure of the methodology requires further verification, including in the context of clarifying theoretical aspects of alexithymia.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".