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PREVALENCE OF ALEXITHYMIA AMONG ADOLESCENTS IN THE CENTRAL AND SOUTHERN REGIONS OF SIBERIA

2022· article· en· W4323287539 on OpenAlexaboutno aff
Zhanna G. Zaitseva, Tatyana A. Kolodyazhnaya, О. И. Зайцева, Irina A. Ignatova

Bibliographic record

VenueSiberian Journal of Life Sciences and Agriculture · 2022
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaResidenceEthnic groupClinical psychologyDemographyPersonalityMedicinePsychologyPsychiatry

Abstract

fetched live from OpenAlex

Aim. To study the prevalence of alexithymia among adolescents in the central (Krasnoyarsk city/Caucasians) and southern (Abakan, the Republic of Khakassia, Caucasians/Khakasses) regions of Siberia, taking into account ethnicity and gender differences. Materials and methods. Interviewing schoolchildren of various ethnic backgrounds aged 15-18 years is carried out using the Toronto Alexithymic Scale (TAS-26). Results. Mong Caucasian adolescents from different regions of residence (Krasnoyarsk and Abakan, the Republic of Khakassia), the prevalence rates of alexithymia were comparable (30,0% and 34,2%). For Khakas teenagers in Abakan, the Republic of Khakassia, this indicator (56.3%) was statistically significantly higher (p<0,001) in comparison with Caucasian teenagers, which we regard as a manifestation of the ethno-specific component of the Khakas personality in adolescence. Gender differences were established, manifested by a small number of non-alexithymic Khakass girls (6,8%) in comparison with Khakass boys (p=0,0360). Conclusion. Identification among practically healthy adolescents of subjects with signs of alexithymia is necessary for the timely implementation of preventive measures, and corrective measures should be targeted at girls and boys. Rational implementation of preventive measures and dynamic monitoring of alexithymic adolescents will reduce the risk of psychosomatic disorders and the frequency of adverse outcomes.

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 score0.010
Threshold uncertainty score0.141

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 head0.012
GPT teacher head0.235
Teacher spread0.223 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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