The validity of using self-reports to assess emotion regulation abilities in adults with autism spectrum disorder
Bibliographic record
Abstract
PURPOSE: The current paper focused on the validity of using self-reports to assess emotion regulation abilities in autism spectrum disorders (ASD). To assess this we sought responses to two alexithymia self-reports and a depression self-report at two time points from adults with and without ASD. MATERIALS AND METHODS: An initial sample of 27 adults with ASD and 35 normal adults completed the 20-item Toronto alexithymia scale (TAS-20), the Bermond and Vorst alexithymia questionnaire-form B (BVAQ-B), and the Beck depression inventory (BDI), at test time 1. Of these individuals, 19 ASD and 29 controls participated again after a period ranging from 4 to 12 months. RESULTS: ASD participants were able to report about their own emotions using self-reports. BVAQ-B showed reasonable convergent validity and test-retest reliability in both groups. Scores on both alexithymia scales were stable across the two participant groups. However, results revealed that although the TAS-20 total score discriminated between the two groups at both time points, the BVAQ-B total score did not. Moreover, the TAS-20 showed stronger test-retest reliability than the BVAQ-B. CONCLUSION: ASD participants appeared more depressed and more alexithymic than the controls. The use of the BVAQ-B, as an additional assessment of alexithymia, indicated that ASD patients have a specific type of alexithymia characterised by increased difficulties in the cognitive domain rather than the affective 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".