P161 “If I were white”: A qualitative analysis of the experiences of ethnic minorities with autoimmune rheumatic diseases
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,014 | 0,013 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,003 | 0,012 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».