IDENTIFYING STEREOTYPES AMONG EARLY TRAINEES FOR PATIENTS SYSTEMIC LUPUS ERYTHEMATOSUS
Notice bibliographique
Résumé
PV155 / #552 Poster Topic: AS17 - Miscellaneous Background/Purpose Stereotyping and bias are common in medical care, especially for patients with chronic health conditions or from marginalized backgrounds. While there is extensive research on bias in conditions like fibromyalgia and chronic pain, less is known about how these biases impact patients with autoimmune rheumatic diseases, such as systemic lupus erythematosus (SLE). These patients often face significant health disparities and may encounter bias that contributes to delayed diagnosis and treatment. The primary objective of this study was to document stereotypes of patients with SLE in early-career trainees. Methods Between April and May 2024, Internal Medicine Residents at university teaching hospital were invited via email to participate in a voluntary, anonymous survey on trainee bias, stereotyping, and knowledge of common rheumatic diseases. After consenting, participants answered questions assessing their awareness of stereotypes commonly heard about patients with SLE, rheumatoid arthritis, fibromyalgia, and chronic pain. Respondents were also presented with 4 clinical vignettes, validated by rheumatologists, to assess their management of patients with suspected SLE. The vignettes tested diagnostic accuracy, treatment choices, and management of complications. Participants provided demographic information, including training year, post-training plans, and whether they identified as having a chronic illness or disability. Only respondents who completed the entire survey were included in the analysis. Results A total of 75 internal medicine residents were sent the survey, with 22 completing the survey in its entirety (29% response rate). Respondents demonstrated varying levels of awareness regarding stereotypes associated with chronic illnesses (Figure 1). For SLE, participants largely were aware of positive stereotypes, with resilience (45%) as a commonly heard stereotype and 32% as strong, though 36% identified a stereotype of noncompliance and 64% recognized anxiety as a stereotype in patients with lupus. For rheumatoid arthritis, respondents recognized a mix of stereotypes, with 27% associating strength or resilience with patients, while 36% indicated a stereotype of noncompliance and only 9% referenced laziness. Respondents frequently associated fibromyalgia with noncompliance (59%) and laziness (50%), with limited awareness of resilience (5%) or strength (0%). Chronic pain was similarly linked to noncompliance (73%) and laziness (41%), with some awareness of resilience (18%) and strength (14%). For general chronic illness or disability, noncompliance (91%) and laziness (50%) were common stereotypes, though awareness of resilience (41%) and strength (27%) was also noted. For all disease states respondents indicated that females experience more distressing symptoms than male patients. In knowledge-based questions aimed at assessing the residents as primary care physicians perspective, 59% of respondents correctly identified appropriate timing for referring a patient to rheumatology, 32% referred a patient with a severe lupus flare to the emergency department for urgent evaluation, 82% accurately determined when to order an ANA, and 82% correctly recognized when to treat and monitor the patient in the primary care office. Figure 1. Awareness of common stereotypes for patients with chronic illness and autoimmune rheumatic diseases by early career trainees Conclusions Early-career trainees are aware of stereotypes commonly held by clinicians about chronic illnesses. Negative stereotypes, such as noncompliance and laziness, are particularly prevalent for fibromyalgia and chronic pain, whereas lupus and rheumatoid arthritis are more often associated with positive traits like resilience and strength. By identifying these biases, the research underscores the need for targeted interventions to reduce disparities and support equitable care for patients with autoimmune rheumatic diseases.
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 | 0,003 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».