Medical Students’ Empathy Level Differences by Medical Year, Gender, and Specialty Interest in Akdeniz University
Bibliographic record
Abstract
BACKGROUND: As an important feature in patient-physician communication for both primary and clinical care, empathy is one of the basic competencies that physicians should possess. The primary aim of this study was to evaluate the level of empathy among medical students in all years of medical training using two different instruments: the Jefferson Scale of Physician Empathy (for clinical empathy level) and the Toronto Empathy Questionnaire (for general empathy level). MATERIALS AND METHODS: This study is a cross-sectional descriptive study conducted in 2017-2018 academic year with students studying at Akdeniz University Faculty of Medicine. Data collection form, Toronto Empathy Questionnaire (TEQ) and Jefferson Scale of Physician Empathy (JSPE) was applied to the students by the researchers. The statistical analysis was carried out by using IBM-SPSS version 23 for Mac OS. T-test, ANOVA test, Spearman and Pearson correlation analysis were used for comparisons. RESULTS: The mean TEQ score of the students was 52.8/65 and the JSPE-S score was 80.3/100. TEQ scores of students increased up to 4th year and then decreased, but the difference between the years was not statistically significant. The third year students' JSPE-S scores were significantly higher than that of the sixth year students. CONCLUSION: While the clinical empathy levels of medical students decreased significantly after 3rd year, the general empathy levels decreased less. This result shows us that we should review our medical education curriculum and educational environment, and should initiate initiatives, and devote more time to empathy education in order to prevent the decrease in empathy level and increase empathy during medical education.
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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 | 0.000 |
| 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) | 0.003 | 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".