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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".