ALEXITHYMIA AND THE BIG FIVE PERSONALITY TRAITS AS PREDICTORS OF BURNOUT AMONG MEDICAL STUDENTS
Bibliographic record
Abstract
ALEXITHYMIA AND THE BIG FIVE PERSONALITY TRAITS AS PREDICTORS OF BURNOUT AMONG MEDICAL STUDENTS (Abstract): Professional exhaustion or burnout in health professionals has become a serious problem, as previous studies documented its effects on personal and professional life, such as a lower quality of care, reduced level of patient satisfaction, and problems with patient safety. Aim: Our study aimed to assess the influence of several psychological, demographic and academic characteristics on Romanian medical students’ levels of burnout. Material and methods: Participants (315 students in general medicine) were administered three psychological instruments: Maslach Burnout Inventory, measuring the general level of burnout and its three dimensions: emotional exhaustion, depersonalization and personal accomplishment; Big Five Inventory, evaluating the Big Five Factors (dimensions) of personality: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness; and Toronto Alexithymia Scale, assessing one’s difficulties with emotional processing and emotional awareness on three dimensions: difficulty of describing feelings, difficulty of identifying feelings, and externally-oriented thinking. Results show that students with higher levels of Neuroticism also report higher levels of Emotional Exhaustion. Also, more extravert, conscientious, open, and less neurotic students experience a greater sense of Personal Accomplishment, while students low on Agreeableness score high on Depersonalization. Associations between the three facets of burnout and demographic and academic variables were also found. Conclusions: such psychological profiles of burnout vulnerability among medical students could inform future academic guidance programs designed to prevent burnout at younger stages of medical training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".