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Record W2035566177 · doi:10.2298/sarh1306366t

Serbian translation of the 20-item toronto alexithymia scale: Psychometric properties and the new methodological approach in translating scales

2013· article· en· W2035566177 on OpenAlexaffabout
Nikola N. Trajanovic, Vladimir Djurić, Milan Latas, Srđan Milovanović, Aleksandar Jovanović, Dus̆an Djurić

Bibliographic record

VenueSrpski arhiv za celokupno lekarstvo · 2013
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsToronto Western HospitalUniversity Health Network
Fundersnot available
KeywordsSerbianScale (ratio)AlexithymiaReliability (semiconductor)Consistency (knowledge bases)Sample (material)MedicineClinical psychologyStandardizationPopulationLinguisticsArtificial intelligenceComputer scienceCartography

Abstract

fetched live from OpenAlex

INTRODUCTION: Since inception of the alexithymia construct in 1970's, there has been a continuous effort to improve both its theoretical postulates and the clinical utility through development, standardization and validation of assessment scales. OBJECTIVE: The aim of this study was to validate the Serbian translation of the 20-item Toronto Alexithymia Scale (TAS-20) and to propose a new method of translation of scales with a property of temporal stability. METHODS: The scale was expertly translated by bilingual medical professionals and a linguist, and given to a sample of bilingual participants from the general population who completed both the English and the Serbian version of the scale one week apart. RESULTS: The findings showed that the Serbian version of the TAS-20 had a good internal consistency reliability regarding total scale (alpha=0.86), and acceptable reliability of the three factors (alpha=0.71-0.79). CONCLUSION: The analysis confirmed the validity and consistency of the Serbian translation of the scale, with observed weakness of the factorial structure consistent with studies in other languages. The results also showed that the method of utilizing a self-control bilingual subject is a useful alternative to the back-translation method, particularly in cases of linguistically and structurally sensitive scales, or in cases where a larger sample is not available. This method, dubbed as 'forth-translation' could be used to translate psychometric scales measuring properties which have temporal stability over the period of at least several weeks.

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)0.001
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 score0.459
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.083
GPT teacher head0.293
Teacher spread0.210 · 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

Citations15
Published2013
Admission routes2
Has abstractyes

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