Exploring Nursing Students' Attitudes Toward Transgender Individuals and Dehumanization of Transgender People: The Role of Psychological Characteristics
Bibliographic record
Abstract
?introduction: The discrimination and dehumanization faced by transgender people is particularly intense, both at the societal level and in health services. Purpose: To examine nursing students' attitudes toward transgender individuals and to explore the role of empathy, demographic characteristics, and personality traits in shaping these attitudes. Additionally, to weigh and culturally adapt the Genderism and Transphobia Scale. Methodology: A cross-sectional study was conducted with a sample of two Universities, in Nursing Departments, with data collection via an anonymous questionnaire that included demographics, the Genderism and Transphobia Scale, the Ten-Item Personality Inventory, the Toronto Empathy Scale, and other tools to assess dehumanization. Data were analyzed with descriptive and inductive statistics, and the significance level was set at 0.05. Results: The results showed that the absence of empathy is associated with higher levels of prejudice and dehumanization. Mean values ??for the Transphobia/Genderism category were higher for males (75,831, SD = 36,337) compared to females (51,641, SD = 26,560) and for non-binary individuals (32,000, SD = 5,715), with statistically significant differences (p < 0.001). Empathy (TEQ) had a negative correlation with dehumanization (r = -0.415, p < 0.001) and transphobia (r = -0.480, p < 0.001). Openness to Experience was negatively correlated with transphobia (r = -0.337, p < 0.001). Linear regression models showed that empathy (? = -3.045, p < 0.001) and Openness to Experience (? = -4.070, p < 0.001) explain 34% of the variability in Transphobia/Genderism (R² = 34%). Conclusions: Enhancing empathy and incorporating inclusive education into nursing curricula can help reduce dehumanization and prejudice against transgender people
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".