‘There is no justice in nursing school’: A qualitative analysis of nursing students' experiences of discrimination shared on Reddit
Bibliographic record
Abstract
AIM: To explore nursing students' experiences of stigma and discrimination within nursing programmes as shared on Reddit, and how other Reddit users offer support and guidance. DESIGN: Qualitative interpretive description. METHODS: Through a critical social theory lens, this study draws on students' posts from three nursing subreddits: r/studentnurse, r/nursingstudent and r/nursing. Data were collected from March 2013 to March 2023. Reflexive thematic analysis was conducted to generate broad themes of nursing students' experiences of stigma and discrimination, and how other Reddit users offered support and guidance. RESULTS: A total of 43 posts with 1412 associated comments were included in this analysis, which generated three predominant themes of nursing students' experiences. Nursing students faced stigma and discrimination across contexts, including from peers, nurses and other healthcare providers working in clinical practicum sites, and patients. Nursing students' posts described navigating the impacts and consequences of such experiences, including on well-being, and programme and career success. In contexts where students were often alone in their experiences of stigma and discrimination within their programmes and with few identified supports, Reddit users sought support and community through Reddit. While many comments offered validation and support, challenges of this social media platform included conflicting advice and unhelpful, judgmental messages. CONCLUSIONS: Despite widely articulated social justice commitments in the profession, nursing students continue to experience stigma and discrimination across contexts within their nursing programmes. IMPLICATIONS FOR PROFESSION: Nurses and nurse educators have a responsibility to acknowledge and make visible such experiences, and take direct action to prevent and remediate stigma and discrimination within nursing education. IMPACT: This research contributes to the growing empirical evidence that nursing students' experience stigma and discrimination within nursing programmes and the healthcare system. REPORTING METHOD: Adherence to COREQ guidelines was maintained. 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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".