Understanding empathy and theory of mind in Romanian dental students for improved educational strategies
Bibliographic record
Abstract
Empathy and theory of mind (ToM) are critical components of social cognition that significantly impact patient care. This study evaluates the levels of empathy and ToM among dental students in Romania, examining demographic influences and predictors of high empathy. Utilizing the Toronto Empathy Questionnaire, the Reading the Mind in the Eyes Test and sociodemographic questions, the study involved 300 dental students, 78.3% females, with a median age of 22. Statistical analyses included the chi-square, Mann-Whitney and correlation tests. Logistic regression was used for the prediction of the highest tertile of empathy. The findings revealed that 73.3% of students had higher-than-average levels of empathy. High empathy is associated in a multivariate model with female gender (OR 2.032, 95% CI 1.041-3.963), excellent or very good perceived health status (OR 1.903, 95% CI 1.143-3.167), and theory of mind (OR 1.078, 95% CI 1.011-1.149). However, the year of study and perceived stress levels did not significantly influence empathy scores. A subset of students (25.6%) showing below-average ToM scores raises concerns, emphasizing the need for further investigation and screening of possible related conditions to enable early detection and intervention. These findings underscore the importance of incorporating empathy training and cognitive skill development into dental education to foster patient-centered care and address the emotional and cognitive needs of future practitioners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".