A Mixed Method Study of Nursing Students' Experiences of Discrimination Within Their Programs
Bibliographic record
Abstract
AIM: This research aimed to explore nursing students' experiences and perspectives on discrimination within nursing programs across classroom and clinical contexts, as well as structural discrimination through institutional policies and processes. DESIGN: Convergent mixed methods. METHODS: Survey and individual interviews to capture students' experiences and perspectives on discrimination within nursing programs. RESULTS: Quantitative findings suggest that the majority of nursing students (79.2%) self-reported experiencing discrimination during their nursing program. These experiences stem from racism, homophobia, transphobia, and mental health stigma. While most of these experiences were reported in clinical contexts from nurses, patients, and clinical educators, students also reported experiencing discrimination in classroom and program contexts through nurse educators, peers, and policies. Qualitative findings provided nuanced insights into these discriminatory experiences across contexts and sources. Additionally, findings suggest that the majority of the students perceive stigma and discrimination to be a significant issue within nursing education and recommend priorities for addressing this issue. CONCLUSIONS: Despite nursing professions' central commitment to addressing discrimination and promoting social justice, stigma and discrimination faced by nursing students within nursing programs remain a significant concern. IMPLICATIONS FOR PROFESSION: Preventing discrimination and promoting social justice within nursing is a central responsibility of the nursing profession. Student-identified priorities suggest an upstream approach that involves education for nurse educators and staff to redress ongoing discrimination experienced by nursing students. IMPACT: This research contributes to the growing empirical evidence that nursing students experience discrimination within nursing programs across clinical, classroom, and program contexts and highlights student-identified priorities for addressing discrimination. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".