A critical discourse analysis of nursing's response to anti-Indigenous racism in healthcare
Bibliographic record
Abstract
Background: The impact of colonization, assimilative policies, genocide and the disruption of Indigenous Knowledge systems through the imposition of Western bio-medical supremacy within Canada’s healthcare system has fractured and harmed health and healing practices that have otherwise contributed to healthy Indigenous communities and populations since time immemorial. The harm stemming from colonization and anti-Indigenous racism within healthcare and nursing have resulted in gross inequities and poor health outcomes for Indigenous Peoples in Canada. Nurses play a key role in addressing discourses about anti-Indigenous racism in health systems and the health inequities impacting Indigenous Peoples. The objectives of this thesis are to draw on key policy texts as an entry point for exploring and describing the various discourses, contextual factors, and socio-political trends that influence nursing’s engagement with policies related to Indigenous Peoples’ health. Methods: Primary data was collected from numerous text-based sources via provincial, national and international nursing organizations, including reports, policy products, descriptive articles and print-based intervention materials. This study was guided by critical discourse theory, critical race theory and critical Indigenous theory to analyze the factors, contexts and political trends that influence nursing’s engagement with Indigenous health policies in the Canadian context. Methodologically, critical discourse theory informed the basis of my inquiry in understanding the nature of an intended audience as a variable, where nursing discourse is examined to characterize the audience of nursing regarding its relationships as a profession with Indigenous health policies. Conclusions: These findings have implications for the promotion of Indigenous health and well-being through the disruption of anti-Indigenous racism in health systems by investigating nursing’s role and relationship with Indigenous health policies. In response to intervention materials directed at anti-Indigenous racism in healthcare, this study supports the nursing profession in actioning their positionality to disrupt harmful colonial systems and provide culturally safe healthcare and access for Indigenous Peoples.
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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.022 | 0.031 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".