The prevalence of alexithymia among medical students. The relationship between alexithymia and somatic morbidity, the presence of bad habits and the level of academic performance
Bibliographic record
Abstract
Aim. To determine the prevalence and severity of alexithymia among medical university students. To assess the relationship between somatic pathology in students and academic performance. Material and methods. An anonymous standardized questionnaire survey of 130 students of the Tyumen State Medical University was conducted. The questionnaire is based on a Russian-language validated version of the 20-point Toronto Alexithymia Scale for quantifying alexithymia, including information about age, gender, concomitant psychosomatic diseases, unwanted habits, and self-assessment of academic performance. Results. Among the examined students, the frequency of alexithymia was 6,2%. The risk group for alexithymia included 12.3% of the surveyed students. Diseases of a psychosomatic nature (arterial hypertension, VSD, cephalgia, bronchial asthma, chronic skin diseases) were registered twice as often. Alexithymia is often combined with a number of undesirable habits, among which alcohol abuse, smoking, nail biting, and sweet drinking predominate. The relationship between the presence of alexithymia and reduced academic performance is determined. Conclusion. To reduce the negative impact of alexithymia on somatic and mental health, the effectiveness of training and personal development of students, it is necessary to conduct a systematic assessment of the level of alexithymia. When identifying alexithymia or its risk, use methods of psychotherapeutic correction.
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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 | 0.001 |
| 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".