Attitudes of Public Hospital Nurses in Greece Toward Cultural Diversity and Their Association with Empathy
Bibliographic record
Abstract
This study aimed to investigate the attitudes of public hospital nurses in Greece toward cultural diversity and explore the relationship between these attitudes and empathy. A 4-month cross-sectional quantitative study was conducted from October 2022 to January 2023. The questionnaires were distributed using the Google Forms platform, and the sample comprised 179 nurses (n=179) employed in public hospitals. The questionnaire encompassed demographic and work-related inquiries, along with sections on cross-cultural health education and the Clinical Culture Competency Questionnaire (CCCQ) and Toronto Empathy Questionnaire (TEQ). Data analysis was carried out using SPSS software, version 28. The results indicated that nurses exhibit a moderate to fairly high level of positive attitudes toward ethnic minority patients, alongside a low level of empathy. Additionally, a positive weak correlation was observed between cultural skills and empathy (r=.223, p<.001), as well as between management of cultural situations and empathy (r=.162, p<.005). Significant differences in nurses' attitudes toward cultural diversity and empathy were identified based on specific demographic characteristics and their training experiences in intercultural health. In conclusion, this study underscores the importance of nurturing more positive attitudes among nurses toward patients from ethnic minorities. This necessitates a concerted effort to enhance both empathy and training in intercultural health. By fostering these qualities, healthcare professionals can better address the diverse needs of patients, thereby promoting more inclusive and effective healthcare delivery.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".