PECULIARITIES OF ALEXITYMIA LEVEL AND TYPE OF ATTITUDE TOWARDS THE DISEASE AMONG STUDENTS WITH PSYCHOSOMATIC DISEASES
Bibliographic record
Abstract
The article presents the results of study for peculiarities of alexitymia level and type of attitude towards disease among students with psychosomatic diseases. Urgency of the research is conditioned with the fact that modern society faces the growing problem of psychosomatic diseases, which are diagnosed among students with increasing frequency. Alexitymia influences the human’s perception of the disease and attitude towards own health in whole, which makes the treatment process complicated. As methods, we used questionnaire survey with the aim to disclose the group of patients (conditional) with psychosomatic diseases, «Toronto alexitymia scale» (TAS-26) estimation of alexitymia level and method of «Type of attitude towards disease» for diagnostics of the type of attitude towards disease among students. Significant interrelations between alexitymia level and types of attitude towards disease among students with psychosomatic diseases were disclosed. It was found that students with high alexitymia level much more frequently have psychosomatic diseases compared to students with low alexitymia level. The significance of research is in its practical opportunities for improvement of collaboration of general practitioners and psychologists, which is especially important at treatment of students with psychosomatic diseases. The understanding of interrelation peculiarities of alexitymia and type of attitude towards disease among students gives the opportunity to select the most efficient complex methods of accompaniment and treatment, and promotes the patient retention towards the therapy.
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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 | 0.000 | 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".