Toward decolonizing nursing: the colonization of nursing and strategies for increasing the counter‐narrative
Bibliographic record
Abstract
Although there are notable exceptions, examination of nursing's participation in colonizing processes and practices has not taken hold in nursing's consciousness or political agenda. Critical analyses, based on the examination of politics and power of the structural determinants of health, continue to be marginalized in the profession. The goals of this discussion article are to underscore the urgent need to further articulate postcolonial theory in nursing and to contribute to nursing knowledge about paths to work toward decolonizing the profession. The authors begin with a description of unifying themes in postcolonial theory, with an emphasis on colonized subjectivities and imperialism; the application of a critical social science perspective, including postcolonial feminist theory; and the project of working toward decolonization. Processes involved in the colonization of nursing are described in detail, including colonization of nursing's intellectual development and the white privilege and racism that sustain colonizing thinking and action in nursing. The authors conclude with strategies to increase the counter-narrative to continued colonization, with a focus on critical social justice, human rights and the structural determinants of health.
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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 | 0.031 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.107 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".