Health practitioner regulation and anti-racism: A scoping review
Bibliographic record
Abstract
Racism remains pervasive in healthcare systems, driving inequalities for racialized healthcare professionals and clients. The role of health practitioner regulators is to protect the public; however, their actions or inactions may also perpetuate racism. Though there is increasing recognition of regulators’ role in addressing racism, there is currently no synthesized understanding of existing knowledge on this topic. A scoping review following JBI guidelines explored the literature on racism and health practitioner regulation. Six databases were searched: Ovid MEDLINE, Ovid Embase, CINAHL, Scopus, Web of Science Core Collection, and ProQuest Dissertations and Theses Citation Index. Leading global regulatory organizations were searched for grey literature. Fifty-four sources were included in the review, with 57 % categorized as scholarly literature and 43 % as grey literature. While underdeveloped, this scholarship has grown consistently since 2020. Sources were predominantly from the United Kingdom and the United States, followed by Canada, Australia, and New Zealand. Racism and/or racial discrimination can be manifested and perpetuated within regulators’ organizational practices, complaints and conduct processes, licensure requirements, practice standards or guidelines, education program approval or accreditation processes, and continuing competence programs. Health practitioner regulators can create anti-racist healthcare systems by addressing their organizational governance structures and processes and attending to their core regulatory functions. While progress has been made toward anti-racist health practitioner regulation, further critical analysis and empirical evidence are needed to inform effective strategies. Clarifying concepts, collecting race-based data, partnering with racialized groups, and integrating anti-racism into regulatory performance frameworks can drive impactful reforms. • Racism can be perpetuated or reinforced through health practitioner regulation. • Anti-racism can be integrated into organizational practices and regulatory functions. • Conceptual clarity, race-based data, and performance frameworks are essential. • Partnering with equity scholars and racialized groups in research is a priority.
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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 | 0.017 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".