ANXIETY FACTOR IN ADOLESCENTS OF DIFFERENT POPULATION GROUPS OF SIBERIA WITH ALEXITHYMIA
Bibliographic record
Abstract
Data on the relationship between anxiety and alexithymia in adolescents from different population groups in Siberia are scarce and fragmentary. Purpose. To study the factor of “anxiety” in adolescents of different population groups in Siberia with alexithymia, taking into account age and gender, ethnic origin and regional characteristics, and to establish relationships. Materials and methods. Interviewing adolescents aged 11-18 years using the Toronto Alexithymic Scale (TAS-26) and the “Reactive and Personal Anxiety Scale” by Ch.D. Spielberger – Yu.L. Hanina. Results. Among adolescents of different population groups in Siberia, the frequency of alexithymia in Khakass adolescents is 1.6 times higher in comparison with Caucasian adolescents of the Republic of Khakassia (RKh). Teenagers in Krasnoyarsk occupy an intermediate position. A characteristic sign of alexithymia in adolescents is a high level of anxiety, as evidenced by positive correlations between alexithymia and situational (r=0,47; p<0,001) and personal anxiety (r=0,56; p<0,001). Among all Caucasian adolescents with alexithymia, high situational anxiety is registered 2 times more often in Caucasian adolescents RKH. In terms of the development of a highly anxious alexithymic personality, girls are vulnerable, and by age - older teenagers. Conclusion. Identification of highly anxious alexithymic individuals among practically healthy adolescents is necessary for the timely implementation of preventive measures of a psychological, pedagogical and medical plan, which will reduce the risk of psychosomatic disorders and deviant behavior.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".