Towards abandoning the master’s tools: The politics of a universal nursing identity
Bibliographic record
Abstract
Healthcare environments continue to prove discriminatory and marginalizing towards patients and healthcare workers themselves, which contributes to inequitable health outcomes across lines of socially constructed difference. This content and discourse analysis of nursing identity scholarship asks whether there is a connection between nursing identity and oppressive behaviour by examining the construction of nursing identity and the foundational discourses, sometimes in absentia, that support such a construction. Bourdieu's concepts of social fields and Audre Lorde's concept of the master's house are applied as a framework towards understanding the constructs of power and status in healthcare. The analysis identifies a gap in nursing identity literature where social constructs of difference are not considered. In the reviewed literature, nursing identity has been co-opted by the professionalization project in effort to stabilize a conceptualization of nursing's contribution to healthcare towards gaining professional and societal status. This leads to a monolithic, apolitical representation of nursing identity that erases difference, denies historical influence, universalizes nursing work and further marginalizes marginalized identities. The oppressive and hegemonic nature of a universal nursing identity may contribute to a lack of disciplinary reflexivity about the ongoing influences of foundational discursive constructs of gender, epistemology, power and professional status.
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 | 0.024 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.125 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".