How Different Can You Be and Still Survive? Homogeneity and Difference in Clinical Nursing Education
Bibliographic record
Abstract
The article focuses on a component of a three-year institutional ethnography regarding the construction of cultural diversity in clinical education. Students in two Canadian schools of nursing described being a nursing student as bounded by unwritten and largely invisible expectations of homogeneity in the context of a predominant discourse of equality and cultural sensitivity. At the same time, they witnessed many incidents, both personally and those directed toward other individuals of the same culture, of clinical teachers problematizing difference and centering on difference as less than the expected norm. This complex and often contradictory experience of difference and homogeneity contributed to their construction of cultural diversity as a problem. The authors provide examples of how the perception of being different affected some students' learning in the clinical setting and their interactions with clinical teachers. They will illustrate that this occurred in the context of macro influences that shaped how both teachers and students experienced and perceived cultural diversity. The article concludes with a challenge to nurse educators to deconstruct their beliefs and assumptions about inclusivity in nursing education.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.054 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".