Challenges of Perceived <scp>Self‐Management</scp> in Lupus
Bibliographic record
Abstract
OBJECTIVE: Systemic lupus erythematosus is a chronic autoimmune disease with varied and unpredictable levels of disease activity. The ability to self-manage lupus is important in controlling disease activity. Our objective was to determine levels of patient activation toward self-management in lupus. METHODS: We used baseline results from the MyLupusGuide study, which had recruited 541 lupus patients from 10 lupus centers. We used the Patient Activation Measure (PAM), a validated self-reported tool designed to measure activation toward self-management ability, as our primary variable and examined its association with demographic, disease-related, patient-provider communication and psychosocial variables captured in our study protocol. Univariable and multivariable linear regressions were performed using linear mixed models, with a random effect for centers. RESULTS: The mean ± SD age of participants was 50 ± 14 years, 93% were female, 74% were White, and the mean ± SD disease duration was 17 ± 12 years. The mean ± SD PAM score was 61.2 ± 13.5, with 36% of participants scoring in the 2 lower levels, indicating low activation. Variables associated with low activation included being single, having lower physical health status, lower self-reported disease activity, lower self-efficacy, use of more emotional coping and fewer distraction and instrumental coping strategies, and a perceived lack of clarity in patient-doctor communication. CONCLUSION: Low patient activation was observed in more than one-third of lupus patients, indicating that a large proportion of patients perceived that they are lacking in lupus self-management skills. These results highlight a modifiable gap in perceived self-management ability among patients with lupus.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".