Determination of the Cutoff Threshold on the Bermond-Vorst Alexithymia Questionnaire–20 Form B: A Study of 560 Young Adults
Bibliographic record
Abstract
The aim of the study was to calculate the cutoff scores on the Bermond-Vorst Alexithymia Questionnaire-20 Form B. 560 students completed the Bermond-Vorst Alexithymia Questionnaire-20 Form B and the 20-item Toronto Alexithymia Scale. Using cutoff scores of the French or the original version of the 20-item Toronto Alexithymia Scale as the standard, the participants were divided into alexithymic and non-alexithymic groups. The Bermond-Vorst Alexithymia Questionnaire-20 Form B cutoff scores selection was based on the sensitivity, specificity, receiver operating characteristic curve analysis, and the analyses of the clinical data. The most appropriate cutoff scores for determining the absence and presence of alexithymia ranged from 43 to 45 and from 50 to 53, respectively.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".