P161 “If I were white”: A qualitative analysis of the experiences of ethnic minorities with autoimmune rheumatic diseases
Bibliographic record
Abstract
Abstract Background/Aims Individuals of ethnic minorities within the UK have worse healthcare experiences and poorer health outcomes than their white counterparts. Despite the UK welfare state employing multiple public health and social policies to attempt to reduce the impact of socio-economic inequalities on health, these health inequalities remain. It has been demonstrated that sociodemographic characteristics play an important role in a person’s risk of developing rheumatic disease, their disease progression, and treatment journeys. While research into ethnic inequalities in systemic autoimmune and rheumatic conditions (SARDs) within the USA is extensive, there is limited understanding of the experiences of ethnic minorities in the UK. This study aims to investigate how ethnicity affects the medical and lived experiences of ethnic minorities with SARDs in the UK. Methods We are currently conducting in-depth interviews with participants purposefully selected from the INSPIRE and LISTEN rheumatology research projects to ensure a broad range of sociodemographic characteristics. Interviews to date have been carried out with N = 21 patients (38% S.E Asian and 90% Female), and N = 9 clinicians (67% rheumatologists). Interviewing will continue until data saturation has been reached. Analysis is thematic and involves immersion in the data, coding using NVivo, and discussion of themes with a multidisciplinary team including patient partners. Results Preliminary findings demonstrated that approximately half of the patients in the sample reported experiencing some form of discrimination based on their ethnicity. This included feeling that their ethnicity was a contributing factor to receiving less quality care: “If I were white they would have treated me differently. There is no doubt. I questioned whether they were being prejudiced because I was Korean” (Female lupus patient). Other patients felt that discrimination was often more subtle: “racism, particularly in this country, it’s very sophisticated⋯.you can’t really pinpoint it- it’s like adding garlic to a dish. You know it’s there⋯ But you can’t really prove it” (Female lupus patient). There were examples given of patients feeling typecast by clinicians based on preconceived notions of how people of their ethnicity display disease symptoms or behave. However, other participants reported that their ethnicity had no impact on their care. Some suggested that living in major cities with diverse populations reduced ethnic discrimination. Some clinicians expressed having an awareness of patient-perceived discrimination and communicated more difficulty treating patients of ethnic minorities due to a lack of understanding of their socio-cultural experiences. Conclusion This study is of importance for exploring the experiences and views of clinicians and patients relating to ethnicity. It will also address avenues for improving medical resources, satisfaction with care, and support for SARDs patients of ethnic minorities. Full analyses will be completed by January 2025 and reported at the conference. Disclosure S. Taylor: None. M. Ubhi: None. S. Tayabali: None. R. Modi: None. K. Naidu: None. A. Taiwo: None. M. Piper: None. A. Kaul: None. E. Dunbar: None. W. Diment: None. D. D’Cruz: Corporate appointments; Leadership position on the APS charity board. Consultancies; Consultancy/speaker fees from GSK, Eli Lilly, Vifor and UCB. M. Sloan: None.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".