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Record W2086127431 · doi:10.1111/1467-9450.00205

20‐Item Toronto Alexithymia Scale: Do difficulties describing feelings assess proneness to shame instead of difficulties symbolizing emotions?

2000· article· en· W2086127431 on OpenAlexaboutno aff
Thomas Suslow, Uta‐Susan Donges, Anette Kersting, Volker Arolt

Bibliographic record

VenueScandinavian Journal of Psychology · 2000
Typearticle
Languageen
FieldPsychology
TopicEmotions and Moral Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsShameAlexithymiaPsychologyFeelingToronto Alexithymia ScaleScale (ratio)Social psychologyTraitPersonalityConstruct (python library)Developmental psychologyClinical psychology

Abstract

fetched live from OpenAlex

A hallmark of alexithymia is the difficulty putting emotional states into words which has to be differentiated from problems to communicate emotion to others. Shame proneness is a personality trait that is expected to be closely related to a reduced emotional self-disclosure in social interactions. The present investigation was conducted to examine construct validity of the Difficulties Describing Feelings scale of the 20-Item Toronto Alexithymia Scale (TAS-20). The TAS-20 was administered to 68 subjects (30 psychiatric inpatients and 38 normals) along with the Levels of Emotional Awareness Scale (LEAS), a direct measure of the ability to express feelings verbally, and the Shame-Guilt-Scale. Difficulties Describing Feelings was associated with shame assessing scales but not with guilt assessing scales or the LEAS. Thus, in view of our data one should be cautious in interpreting scores from the TAS-20 scale Difficulties Describing Feelings as indices of a difficulty to symbolize one's emotions. Instead, this TAS-20 scale seems to evaluate aspects of social shame.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.065
GPT teacher head0.355
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations57
Published2000
Admission routes1
Has abstractyes

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Same venueScandinavian Journal of PsychologySame topicEmotions and Moral BehaviorFrench-language works237,207