PREVALENCE OF ALEXITHYMIA AMONG ADOLESCENTS IN THE CENTRAL AND SOUTHERN REGIONS OF SIBERIA
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".