‘ <i>If I Were White</i> ’: a qualitative analysis of the experiences of minoritized ethnic groups with systemic autoimmune rheumatic diseases in the United Kingdom
Bibliographic record
Abstract
BACKGROUND: In the United Kingdom (UK), individuals of minoritized ethnic groups report poorer healthcare experiences and face disparities in health outcomes and access to healthcare services relative to their White counterparts. While it has been demonstrated that sociodemographic characteristics play important roles in the risk of developing rheumatic diseases, disease progression, and treatment journeys, there is limited understanding of the experiences of minoritized ethnic groups in the UK. This study aimed to investigate how the social and structural processes associated with ethnicity affect the medical experiences of people with systemic autoimmune rheumatic diseases in the UK. DESIGN: = 16 (81% White, 50% female) clinicians. Analysis was thematic and involved immersion in the data, coding using NVivo, and discussion of themes with a multidisciplinary team including patient partners. RESULTS: Interviews generated three main themes: (1) subtle and systemic racism in care and society, (2) racialized medical and behavioural stereotyping, and (3) socio-cultural factors impacting doctor-patient communication and rapport building. Throughout each theme, participant recommendations for improving care were raised. CONCLUSIONS: Our study demonstrated that the socio-structural processes related to ethnicity, namely racism, social deprivation, stereotyping and institutional bias, impact the medical experiences of SARDs patients in multitudinous ways. Some patients reported systemic and interpersonal racism, racialized stereotyping, and mistrust in care, while others listed factors that they considered were protective against discrimination, such as education and location. Socio-cultural factors, including language barriers and variations in clinician understandings of patient experiences, further impact doctor-patient interactions.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 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".