Cultural sensitivity as a problematic in Ontario nursing policy and education: an integrated feminist con
Bibliographic record
Abstract
Transcultural nursing (TCN) theory is one of the most visible forces shaping the dialogue about inclusivity in nursing. TCN theory advances a liberal, humanistic approach to race and race difference that frames nursing research, policy, education, and practice. Using an integrated feminist con/textual analysis, this research interrogated the articulation of TCN theory in three sets of nursing texts that organize the social relations among nurses and between nurses and clients. This investigation highlights the links between a College of Nurses of Ontario (CNO) standard of practice on culture care, curricular texts introducing cultural sensitivity and the TCN literature that supported the development of both in two concrete locations. The institutional texts and the contexts in which they operate were viewed in light of the anti-racism discourse advanced in sociological and educational literature. My general orientation was to explore what nursing institutions were saying about race and race difference through the texts they developed and used, and to expose and name the logical implications of this discourse. From there, I imagined what these same institutions might say and do in moving toward a more inclusive, anti-racism nursing discourse. Conclusions emerging from my analysis demonstrate the power of nursing con/texts to assert the dominant white discourse about race and other social differences that sustains rather than challenges the racialized social order. These findings contribute to the theory and practice of nursing and the broader dialogue about inclusive education in three ways: First, these conclusions illustrate the links between TCN literature, CNO policy and educational practice, and the value of looking to anti-racism discourse for a revisioning of inclusive education. Second, a set of diagnostic questions provides a tool for exposing racist discourse in textual knowledges. Third, using a broadly defined discursive framework, I identify spaces for systemic change in a revisioning of inclusive nursing research, policy and curricula.
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 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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.035 | 0.050 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".