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Record W1997708629 · doi:10.2202/1548-923x.1005

How Different Can You Be and Still Survive? Homogeneity and Difference in Clinical Nursing Education

2003· article· en· W1997708629 on OpenAlexaffabout
Barbara Paterson, Margaret S. Osborne, David Gregory

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsUniversity of ManitobaUniversity of British Columbia
FundersEastern Kentucky University
KeywordsCultural diversityNurse educationPerceptionEthnographyPsychologyNorm (philosophy)Diversity (politics)SociologyNursingPedagogySocial psychologyMedicineEpistemology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.143
GPT teacher head0.476
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2003
Admission routes2
Has abstractyes

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