Alexithymia—A Neglected Factor Influencing Educational Satisfaction and Psychological Wellbeing in Medical Students
Bibliographic record
Abstract
ABSTRACT: Alexithymia presents a reduced ability to identify, express, and describe one's own emotions. In medical professionals, alexithymia is associated with increased burnout and substance use, as well as reduced altruistic behavior. Our aim was to examine the association between alexithymia and different sociodemographic, psychological, and somatic parameters among medical students. Participants included 186 third- and sixth-year students of the Faculty of Medicine in Belgrade, Serbia. Data were collected through an online survey, composed of 1) a general questionnaire (sociodemographic data, education satisfaction, grade point average, presence of psychological and somatic disorders), 2) Twenty-Item Toronto Alexithymia Scale, 3) Patient Health Questionnaire-9, and 4) Beck Anxiety Inventory. Third-year students had significantly higher rates of alexithymia compared with sixth-year students. Negative correlation was found between alexithymia and educational satisfaction ( r = -0.276**) and alexithymia and grade point average ( r = -0.186*). A positive correlation was found between alexithymia and depression ( r = 0.424**) and alexithymia and anxiety ( r = 0.338**). The negative impact of alexithymia on educational satisfaction and psychological health is pronounced in the population of medical students, indicating a need for preventive programs aimed in medical schools.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".