Toward the Development of a Lupus Interactive Navigator to Facilitate Patients and Their Health Care Providers in the Management of Lupus: Results of Web-Based Surveys
Bibliographic record
Abstract
BACKGROUND: Systemic lupus erythematosus is an inflammatory autoimmune disease associated with high morbidity and unacceptable mortality. Information and management tools are needed to help persons with lupus cope with their illness and facilitate health care providers in the delivery of care. OBJECTIVE: The objective of the study was to assess the needs and find solutions to support persons with lupus and their health care providers. METHODS: Web-based surveys were distributed across Canada to persons with lupus and their relatives (n=3119), rheumatologists (n=517), and arthritis health professionals (AHPs) (n=226) by Lupus Canada, the Canadian Rheumatology Association, and the Arthritis Health Professions Association, respectively. RESULTS: The survey sample comprised 665 (21.3%) persons with lupus, 98 (19.0%) rheumatologists, and 74 (32.7%) AHPs. Among the participants with lupus, 92.4% were female, the average age was 46.8 (SD 12.7) years, 79.2% were Caucasian, and 58.8% were employed. All Canadian provinces and territories were represented. The majority (43.3%) of respondents were from Ontario. Mean disease duration was 10.2 (SD 9.5) years, and 41.9% rated their global assessment as fair or poor. There was high agreement between lupus participants and health care providers regarding disease-specific information topics. All groups rated topics related to lupus, fatigue, medications, and stress as most important. Ratings differed among lupus participants and their health care providers regarding perceived helpfulness of some of the patient tools, such as the option to view test results. Needs differed for persons with lupus based on age, sex, depression, stress, and disease activity. Differences in health care provider needs were based on amount of experience in treating lupus. CONCLUSIONS: Information and support tools needed for persons with lupus and their health care providers were identified. These results will help guide us in the development of a Web-based Lupus Interactive Navigator as an intervention tool to help persons with lupus self-manage their disease and to facilitate heath care providers in clinical management.
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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 | 0.034 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".