Specificity in cognitive processing of dizziness descriptors in adolescents with high levels of alexithymia
Bibliographic record
Abstract
The problems of cognitive processing of units describing various diseases are relevant to modern linguistics and interdisciplinary fields. Individuals with high levels of alexithymia experience particular difficulties in adequately perceiving units denoting sensations. Alexithymia is a specific type of cognitive status characterized by difficulties processing sensations, emotions, and feelings, as well as difficulties in their verbal externalization, which poses a significant challenge to clinical practices. A series of linguistically valid diagnostic instruments (scales and questionnaires) have already been developed for use in clinical practices for adult Russian-speaking alexithymic respondents, and a toolkit for children and adolescents is being developed. This article describes a procedure for studying how adolescents with low and high levels of alexithymia understand the descriptors used to diagnose dizziness and analyzes free descriptions of units associated with this state. The study included 50 adolescents aged 13 to 17 with low levels of alexithymia (control group) and 50 adolescents aged 13 to 17 with high levels of alexithymia (study group). Alexithymia was measured using the Alexithymia Questionnaire for Children (Russian version). Based on the linguistic analysis of the data, a dizziness questionnaire for adolescents with high levels of alexithymia was developed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".