Relationship of mental health and burnout with empathy among medical students in Thailand: A multicenter cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: To explore mental health, burnout, and the factors associated with the level of empathy among Thai medical students. BACKGROUND: Empathy is an important component of a satisfactory physician-patient relationship. However, distress, including burnout and lack of personal well-being, are recognized to affect a lower level of empathy. MATERIAL AND METHODS: A cross-sectional study surveyed sixth-year medical students at three faculties of medicine in Thailand at the end of the 2020 academic year. The questionnaires utilized were: 1) Personal and demographic information questionnaire, 2) Thai Mental Health Indicator-15, 3) The Maslach Burnout Inventory-Thai version, and 4) The Toronto Empathy Questionnaire. All data were analyzed using descriptive statistics, and factors associated with empathy level were analyzed via the Chi-square test or Fisher's exact test, logistic regression., and linear regression. RESULTS: There were 336 respondents with a response rate of 70.3%. The majority were female (61.9%). Most participants reported a below-average level of empathy (61%) with a median score (IQR) of 43 (39-40). Assessment of emotion comprehension in others and altruism had the highest median empathy subgroup scores, whereas behaviors engaging higher-order empathic responses had the lowest median empathy subgroup score. One-third of participants (32.1%) had poor mental health, and two-thirds (62.8%) reported a high level of emotional exhaustion even though most of them perceived having a high level of personal accomplishment (97%). The multivariate analysis indicated that mental health was statistically significantly associated with the level of empathy. The participants with higher levels of depersonalization had statistically lower scores of demonstrating appropriate sensitivity, altruism, and behaviors engaging higher-order empathic responding. CONCLUSIONS: Most medical students had below-average empathy levels, and two-thirds of them had high emotional exhaustion levels, yet most of them reported having a high level of personal accomplishment and good mental health. There was an association between mental health and the level of empathy. Higher levels of depersonalization related to lower scores of demonstrating sensitivity, altruism, and behaviors responding. Therefore, medical educators should pay close attention to promoting good mental health among medical students.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".