The Relationship between Mental Health with the Level of Empathy Among Medical Students in Southern Thailand: A University-Based Cross- Sectional Study
Bibliographic record
Abstract
Objective: To determine the level of and factors associated with empathy among medical students.Materials and Methods: This cross-sectional study surveyed all first- to sixth-year medical students at the Facultyof Medicines, Prince of Songkla University, at the end of the 2020 academic year. The questionnaires consisted of:1) The personal and demographic information questionnaire, 2) The Toronto Empathy Questionnaire, and 3) ThaiMental Health Indicator-15. Data were analyzed using descriptive statistics, and factors associated with empathylevel were assessed via chi-square and logistic regression analyses.Results: There were 1010 participants with response rate of 94%. Most of them were female (59%). More than half(54.9%) reported a high level of empathy. There was a statistically significant difference in empathy levels betweenpre-clinical and clinical medical students; in regards to empathy subgroups (P-value < 0.001). The assessment ofemotional states in others by demonstrating appropriate sensitivity behavior, altruism, and empathic respondingscores among the pre-clinical group were higher than those of the clinical group. Multivariate analysis indicatedthat female gender, pre-clinical training level, and minor specialty preference were factors associated with empathylevel. The protective factor that significantly improved the level of empathy was having fair to good mental health.Conclusion: More than half of the surveyed medical students reported a high level of empathy. The protective factorthat improved the level of empathy was good mental health. However, future qualitative methods, longitudinalsurveillance, or long-term follow-up designs are required to ensure the trustworthiness of these findings.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".