Assessment of alexithymia in patients with chronic illness: a comparison between the self-report 20-item Toronto alexithymia scale and the informant formCamelio Martina, Zito Luigia
Bibliographic record
Abstract
Introduction: The self-report Toronto Alexithymia Scale-20 (TAS-20) is considered the gold standard of the assessment of alexithymia. A recognized limitation of TAS-20 is the lack of ability of alexithymic individuals in identifying and describing their own feelings, a sort of assessment paradox. To address this issue the Informant Form of TAS-20 (TAS-20-IF) has been proposed. The aim of this pilot study is to compare the TAS-20 with TAS-20-IF in patients with chronic illness. Methods: Sixty-four patients (n=46 with obesity and n=18 with chronic pain) completed the TAS-20 and their informants (N=64) completed the TAS-20-IF. Results: Multiple significant correlations in the moderate-to-high range (r>.30) were found between the two versions, except for the EOT subscale. No statistically significant differences between the TAS-20 and the TAS-20-IF were found in total scores and their subscales. Only scores of items 13 (t=2.23, p=.02) and 19 (t=1.98, p=.05) were significantly greater in TAS-20 than TAS-20-IF whereas the opposite results were found for items 12 (t=3.45, p=.001) and 18 (t=2.01, p=.04). Conclusions: In this study, except for four items (two of which within the EOT factor), no significant differences were found between TAS-20 and TAS-20-IF. Further research with larger samples is needed.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".