Special Issue on Anti-Racism, Health, and Nursing
Bibliographic record
Abstract
Although race is socially constructed, racism and racialization are social determinants of health. Over four centuries of colonial genocide and structural violence against Indigenous and Black peoples in Canada have resulted in intergeneration traumas and health disparities among Indigenous and Black people, sustained by ongoing social, political, and economic inequities. Evidence indicates the impact of contemporary and historical forms of racism on health outcomes. This special issue invited papers that could contribute to our understanding of the role of racism in nursing and health in Canada as well as solutions to tackle racism in healthcare and the nursing profession. Our call for proposals produced around 16 articles, all of which provide critical insight to address racism in nursing and healthcare. These articles explore the experience of racism in Indigenous, Black, Asian, and other populations across education, clinical, and community settings. They also advance our understanding on philosophical and theoretical approaches to address racism and provide us with effective tools and insight to address racism in nursing and healthcare in Canada.
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.006 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".