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Record W2135198903 · doi:10.1177/136346150103800305

Cross-Cultural Validation of the Toronto Alexithymia Scale (TAS-20) in U.S. and Peruvian Populations

2001· article· en· W2135198903 on OpenAlexaffabout
Carmen G. Loiselle, Sylvie Cossette

Bibliographic record

VenueTranscultural Psychiatry · 2001
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsUniversité de MontréalMcGill University
Fundersnot available
KeywordsAlexithymiaOperationalizationPsychologyConstruct (python library)Toronto Alexithymia ScaleConstruct validityConfirmatory factor analysisScale (ratio)IntrospectionEquivalence (formal languages)Cross-cultural studiesPsychometricsSample (material)Internal consistencyDevelopmental psychologySocial psychologyClinical psychologyStructural equation modelingCognitive psychologyGeographyCartography

Abstract

fetched live from OpenAlex

The cross-cultural relevance of alexithymia, a psychological construct related to emotional expressiveness, is explored through construct validation using the 20-item Toronto Alexithymia Scale (TAS-20) and two theoretically related concepts – patient self-disclosure (SD) and private selfconsciousness (PSC) – among English-speaking Americans ( N= 333) and Spanish-speaking Peruvians ( N= 228). In the American sample, the TAS-20 showed psychometric properties similar to those reported elsewhere with North American and European samples. However, with the Peruvian sample, the Spanish version of the TAS-20 had low internal consistency, low mean inter-item correlations and the original three-factor structure could not be duplicated. Semi-structured interviews with Peruvian informants ( n= 10) point to difficulties responding to negatively keyed items and low reliance on introspection when describing affective states. In light of these findings, issues related to translation adequacy, measurement error and cross-cultural equivalence in construct operationalization are discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.016
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

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

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 head0.022
GPT teacher head0.321
Teacher spread0.299 · 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 teacher head, 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

Citations33
Published2001
Admission routes2
Has abstractyes

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