Dehumanization and attitudes toward LGBTQ individuals among primary healthcare nurses: The role of personality traits and LGBTQ health knowledge
Bibliographic record
Abstract
OBJECTIVE: Aim: This study investigates dehumanization and attitudes toward LGBTQ+ individuals among primary healthcare nurses in Greece, exploring the influence of personality traits, empathy, and LGBTQ+ health knowledge. PATIENTS AND METHODS: Materials and Methods: A cross-sectional design was used with 114 public-sector primary healthcare nurses completing self-report questionnaires between July and October 2023. Instruments included a culturally adapted dehumanization scale, the Ten-Item Personality Inventory, and the Toronto Empathy Questionnaire. Statistical analysis included Mann-Whitney and Kruskal-Wallis tests, Spearman's correlations, and linear regression. RESULTS: Results: The sample was predominantly female (74.6%), heterosexual (93.9%), and Christian Orthodox (93%). Only 8.8% had attended LGBTQ+ healthcare courses, and 33.3% had cared for LGBTQ+ patients. Mechanistic dehumanization showed limited associations with personality traits, while animalistic dehumanization was negatively correlated with willingness to care (r = -0.441, p < 0.001) and comfort with LGBTQ+ care (r = -0.391, p < 0.001). Empathy and openness to experience influenced attitudes and willingness to care. Higher empathy unexpectedly reduced willingness to care, while emotional stability and conscientiousness predicted dehumanization. CONCLUSION: Conclusions: Findings highlight a moderate dehumanization trend among nurses, affecting LGBTQ+ patients' care quality. Educational initiatives targeting LGBTQ+ health knowledge, empathy training, and the influence of personality traits are critical to fostering inclusive care and reducing dehumanization in healthcare settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".