Cultivating ethnocultural empathy in healthcare: The effects of multilingualism and cross-cultural experiences
Bibliographic record
Abstract
Introduction: Empathy is a multidimensional construct essential to effective healthcare delivery, encompassing general, clinical, and ethnocultural components. While the importance of empathy in clinical settings is well-established, limited research has examined how these distinct dimensions interrelate and what sociodemographic factors may influence them. This study aimed to investigate the relationships among general, clinical, and ethnocultural empathy in healthcare professionals in Greece, and to identify sociodemographic predictors of higher empathy levels. Methods: A cross-sectional study was conducted during the first quarter of 2022, involving a convenience sample of 106 healthcare professionals (medical and nursing staff) from public hospitals across Greece. Participants completed an electronic questionnaire distributed via professional Facebook groups. The instrument included the Toronto Empathy Questionnaire (TEQ), the Jefferson Scale of Empathy – Health Professional Version (JSE-HP), the Scale of Ethnocultural Empathy (SEE), and a sociodemographic survey. Data were analyzed using SPSS 26.0, employing descriptive statistics, Pearson correlation coefficients, and stepwise linear regression. Results: Significant positive correlations were observed among general, clinical, and ethnocultural empathy scores. Higher levels of education, foreign language proficiency, and prior intercultural experiences (such as studying or living abroad) emerged as significant predictors of increased empathy across all three domains. Discussion: The findings support the interconnected nature of empathy types and suggest that intercultural exposure may enhance empathic capacity, echoing Allport’s contact hypothesis. These insights have implications for healthcare education, highlighting the need to integrate ethnocultural empathy training to foster inclusivity and improve patient-centered care in increasingly diverse clinical environments.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.006 |
| 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".