IDENTIFYING STEREOTYPES AMONG EARLY TRAINEES FOR PATIENTS SYSTEMIC LUPUS ERYTHEMATOSUS
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".