Implementing Anti-Racism Interventions in Healthcare Settings: A Scoping Review
Bibliographic record
Abstract
Racism towards Black, Indigenous and people of colour continues to exist in the healthcare system. This leads to profound harm for people who use and work within these settings. This is a scoping review to identify anti-racism interventions in outpatient healthcare settings. Searching the peer-reviewed and grey literature, articles were screened for inclusion by at least two independent reviewers. Synthesizing the socio-ecological levels of interventions with inductively identifying themes, a conceptual model for implementing anti-racism interventions in healthcare settings is presented. In total, 37 peer-reviewed articles were included in the review, with 12 empirical studies and 25 theoretical or conceptual papers. Six grey literature documents were also included. Healthcare institutions need to incorporate an explicit, shared language of anti-racism. Anti-racism action should incorporate leadership buy-in and commitment with dedicated resources, support and funding; a multi-level approach beginning with policy and organizational interventions; transparent accountability mechanisms for sustainable change; long-term meaningful partnerships with Black, Indigenous, and people of colour (i.e., racialized communities); and ongoing, mandatory, tailored staff education and training. Decision-makers and staff in healthcare settings have a responsibility to take anti-racism action and may improve the success and sustainability of their efforts by incorporating the foundational principles and strategies identified in this paper.
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 | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".