Factors associated with the improvement of the empathy levels among clinical-year medical students in Southern Thailand: a university-based cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Empathy is one of the core medical professionalisms that distress, burnout, and lack of personal well-being is also recognized as an important influencer on lower empathy levels. Therefore, this study aimed to explore the mental health, burnout, and factors associated with the empathy levels among Thai, clinical-year medical students. METHODS: This cross-sectional study surveyed all fourth-to sixth-year medical students at the Faculty of Medicine, Prince of Songkla University, in Thailand; at the end of the 2020 academic year. The questionnaires utilized were: (1) The personal and demographic information questionnaire, (2) The Toronto Empathy Questionnaire, (3) Thai Mental Health Indicator-15, and (4) The Maslach Burnout Inventory; Thai version. All data were analyzed using descriptive statistics, and factors associated with empathy levels were analyzed via the chi-square test and logistic regressions. RESULTS: There were 466 participants, with a response rate of 91.5%. The majority were female (56.2%), and reported a below-average level of empathy (57.1%); with a median score (IQR) of 44 (40-48). The gender proportion of a below-average empathy level among male and female participants was 66.3% and 50.4%, respectively. Of the participants, 29.6% had poor mental health, 63.5% and 39.7% reported a high level of emotional exhaustion and depersonalization scores; even though most of them (96.6%) perceived having a high level of personal accomplishment. Multivariate analysis indicated that females, higher mental health, and a low level of depersonalization were statistically significant protective factors, which improved the empathy levels. CONCLUSIONS: More than half of the clinical-year medical students reported below-average empathy levels. Female gender, better mental health, and a low level of depersonalization were related to the improvement of the empathy levels. Therefore, medical educators should emphasize the importance of focusing supporting students, of all genders and in regards to all stages of medical education, to increase their levels of empathy, to promote individual well-being, and to effectively prevent the phenomenon of student 'burnout'.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".