Reliability of Greek version of the Toronto empathy questionnaire in medical students and associations with sociodemographic and lifestyle factors
Bibliographic record
Abstract
Abstract Background Empathy is an important key driver of any therapeutic relationship. It is beneficial for both physicians and patients. Enhancing physician’s empathy should be an important goal of medical education. As there was a literature gap regarding the topic of empathy among medical students in Greece, this study aimed to contribute to filling this gap. Methods A cross-sectional study was conducted. A socio-demographic questionnaire and the 52-item Greek version of the Toronto composite empathy scale (TCES) for measuring the cognitive and emotional aspects of empathy in both personal and professional life was administered to all the medical students in the Aristotle University of Thessaloniki, in Greece. Descriptive statistics were displayed for demographics. The associations of the variables were quantified by Chi-2 independence tests and Pearson’s Correlation Coefficient. The reliability and validity of the questionnaire was determined by Cronbach’s α, Hotelling's T-Squared Test, and Pearson correlation. Paired and Independent Sample T-Tests and One-way ANOVAs indicated statistically significant mean differences among the variables or subgroups of the variables. Results The 52‐item TCES, 26 for the personal (Per) setting and another 26 for professional (Pro) life, equally divided into cognitive (Cog) and emotional (Emo) empathy in each case. The overall reliability of the TCES questionnaire was found to be high (Cronbach's α = 0.895, significant positive correlations between the subscales). The mean total score of empathy showed that students had a moderately high empathy. Further, there was a statistically significant difference in means between the Per-Cog and Per-Emo settings (p
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".