Psychometric Properties of the Chinese Bermond–Vorst Alexithymia Questionnaire: An Exploratory Structural Equation Modeling Study
Bibliographic record
Abstract
The Toronto Alexithymia Scale-20 (TAS-20) has been widely used to assess alexithymia. The Bermond–Vorst Alexithymia Questionnaire (BVAQ) assesses two additional features of alexithymia—the affective factors of emotionalizing and fantasizing, which are not included in the TAS-20. However, there is currently no Chinese version of the BVAQ. Here, the authors collected data from 439 college students (293 females, aged 17–27, mean ± SD = 20.25 ± 1.88) to evaluate the psychometric properties for a Chinese BVAQ translation. Exploratory structural equation modeling and confirmatory factor analysis provided satisfactory validity and acceptable reliability for a six-factor first-order solution of a 35-item Chinese BVAQ. This adaptation retained the five original BVAQ factors (identifying, analyzing, verbalizing, emotionalizing, and fantasizing) and further specified the factor of identifying (successful identifying and unsuccessful identifying feelings). The authors also found a two-factor second-order model of cognitive and affective components for alexithymia in the Chinese population. Higher correlations with the TAS-20 were observed for identifying, analyzing, and verbalizing feelings (0.34 ∼ 0.61) relative to fantasizing and emotionalizing (0.02 ∼ −0.05). These results support the construct validity of the adaptation. This work provides a reliable and valid Chinese adaptation of the BVAQ.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".