Immigrant Nurses' Experience of Racism
Bibliographic record
Abstract
PURPOSE: To document and describe the experiences of immigrant nurses of colour who have filed grievances concerning their employers' discriminatory practices; and to solicit their views of existing policies and recommendations for equity in professional life. DESIGN AND METHODS: In this descriptive, exploratory study nine immigrant nurses of colour in Ontario, Canada, were interviewed between 1997 and 1998. Data were collected through face-to-face interviews and in focus groups. The discourse theory and methods of van Dijk and Essed were used to analyse the qualitative data. FINDINGS: Recurring themes were: (a) being marginalized and acknowledging and naming the racist experiences; (b) experiencing physical stress and emotional pain; (c) strategizing to cope and survive; (d) recommending policy changes. CONCLUSIONS: All nurses interviewed had experienced reprisals as a result of complaining or filing grievances and unfairness was encountered in the redress process itself. Participants recommended policy initiatives to ensure equity and fair practices in the nursing profession.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".