Black nurse in white space? Rethinking the in/visibility of race within the Australian nursing workplace
Bibliographic record
Abstract
This article presents an analysis of data from a critical qualitative study with 14 skilled black African migrant nurses, which document their experiences of nurse-to-nurse racism and racial prejudice in Australian nursing workplaces. Racism generally and nurse-to-nurse racism specifically, continues to be under-researched in explorations of these workplaces; when racism is researched, the focus is nurse-to-patient racism and racial prejudice. Similarly, research on the experiences of migrant nurses from a variety of ethnicities in Australia has tended to neglect their experiences of the social dynamics of the workplace, thus reinforcing their racialisation. When racialised, the migrant nurse becomes 'the problem' through a focus on English language competency and ensuing communication barriers. This paper applies Essed's framework of 'everyday racism' to theorise narratives of racism by black African migrant nurses in Australia. In so doing, it not only brings to the fore silenced discussions of nurse-to-nurse racism in Australia, but also exposes the subtle, mundane nature of contemporary racism. For this reason, while the data we present must be read within their context, that is, the Australian nursing workplace, it has significance for advancing a critical analysis of racialised minority groups' experiences of racism within seemingly 'race-less' nursing workplaces internationally.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.025 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| 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".