The Bermond-Vorst Alexithymia Questionnaire Cutoff Scores: A Study in Eating-Disordered and Control Subjects
Bibliographic record
Abstract
BACKGROUND: The evaluation of alexithymic deficits has become increasingly desirable in health and psychopathology research. The purpose of this study was to calculate alexithymia cutoff scores for a recently developed self-report alexithymia questionnaire: the Bermond-Vorst Alexithymia Questionnaire Form B (BVAQ-B). SAMPLING: Three hundred subjects (47 eating-disordered patients and 253 healthy individuals) completed the BVAQ-B and the 20-item Toronto Alexithymia Scale (TAS-20). METHODS: The TAS-20 was used as a gold standard for this research, with its previously established cutoff scores serving as diagnostic criteria for determining the presence or absence of alexithymia. The BVAQ-B cutoff score selection was based on the examination of psychometric data (i.e., the sensitivity and specificity of the BVAQ-B scores and receiver operating characteristic curve analyses) and of clinical data (i.e., BVAQ-B mean score of the control subjects, who were mostly nonalexithymic, and BVAQ-B mean score of a group of patients with eating disorders, the majority of whom were alexithymic). RESULTS: This research found that the most appropriate BVAQ-B cutoff scores for determining the absence and presence of alexithymia were 43 and 53, respectively. CONCLUSION: In light of these findings, we believe that the BVAQ-B may also lend itself to a categorical evaluation of alexithymia, with these cutoff scores determining its absence or presence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".