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 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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".