Racism against healthcare users in inpatient care: a scoping review
Bibliographic record
Abstract
BACKGROUND: Racism in the healthcare system has become a burgeoning focus in health policy-making and research. Existing research has shown both interpersonal and structural forms of racism limiting access to quality healthcare for racialised healthcare users. Nevertheless, little is known about the specifics of racism in the inpatient sector, specifically hospitals and rehabilitation facilities. The aim of this scoping review is therefore to map the evidence on racial discrimination experienced by people receiving treatment in inpatient settings (hospitals and rehabilitation facilities) or their caregivers in high-income countries, focusing specifically on whether intersectional axes of discrimination have been taken into account when describing these experiences. METHODS: Based on the conceptual framework developed by Arksey and O'Malley, this scoping review surveyed existing research on racism and racial discrimination in inpatient care in high-income countries published between 2013 and 2023. The software Rayyan was used to support the screening process while MAXQDA was used for thematic coding. RESULTS: Forty-seven articles were included in this review. Specifics of the inpatient sector included different hospitalisation, admission and referral rates within and across hospitals; the threat of racial discrimination from other healthcare users; and the spatial segregation of healthcare users according to ethnic, religious or racialised criteria. While most articles described some interactions between race and other social categories in the sample composition, the framework of intersectionality was rarely considered explicitly during analysis. DISCUSSION: While the USA continue to predominate in discussions, other high-income countries including Canada, Australia and the UK also examine racism in their own healthcare systems. Absent from the literature are studies from a wider range of European countries as well as of racialised and disadvantaged groups other than refugees or recent immigrants. Research in this area would also benefit from an engagement with approaches to intersectionality in public health to produce a more nuanced understanding of the interactions of racism with other axes of discrimination. As inpatient care exhibits a range of specific structures, future research and policy-making ought to consider these specifics to develop targeted interventions, including training for non-clinical staff and robust, transparent and accessible complaint procedures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".