‘Information is power’: A qualitative exploration of co-producing education resources about cardiovascular disease in partnership with women living with lupus
Bibliographic record
Abstract
BACKGROUND: Systemic lupus erythematosus (SLE) is a chronic autoimmune condition impacting 1 in 2000 people. As 90% of SLE patients are women, and racialized populations experience significantly poorer outcomes, we characterize SLE as gendered, racialized, invisible and episodic. Further compounding these inequities is an elevated risk of cardiovascular disease (CVD) among this population. Crucially, there is a dearth of research evidence as well as public knowledge pertaining to its aetiology and manifestations in women. Indeed, CVD is a primary driver of morbidity and mortality in SLE. Despite calls for improved screening and awareness among this high-risk population, there is a lack of risk prediction tools and patient education resources specific to SLE. OBJECTIVES: The objectives of this study were to co-create a lay language patient education resource in partnership with SLE knowledge users, as well as to obtain recommendations on the development of an associated future CVD risk prediction tool designed specifically for this population. DESIGN/METHODS: = 5), respectively. An integrated knowledge translation approach included a transdisciplinary team of researchers and a patient partner throughout the research process. RESULTS: Participants were knowledgeable about SLE but less informed about the risks of CVD. Few recalled discussing CVD with physician(s), but most were aware of differences in symptoms among men and women. Participants responded positively to the education resource and provided recommendations to improve accessibility, inclusivity and impact for the target audience. Participants agreed that they would use the future SLE-CALCULATOR tool and advised on its design and usability. CONCLUSIONS: These results underscore CVD as an urgent women's health issue and highlight the need for inclusive patient education about the risks of CVD in SLE. The resource discussed herein begins to fill that gap.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".