Patient Expectations and Perceptions of Goal-setting Strategies for Disease Management in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To identify how patients perceive the broad effect of active rheumatoid arthritis (RA) on their daily lives and indicate how RA disease management could benefit from the inclusion of individual goal-setting strategies. METHODS: Two multinational surveys were completed by patients with RA. The "Good Days Fast" survey was conducted to explore the effect of disease on the daily lives and relationships of women with RA. The "Getting to Your Destination Faster" survey examined RA patients' treatment expectations and goal-setting practices. RESULTS: Respondents from all countries agreed that RA had a substantial negative effect on many aspects of their lives (work productivity, daily routines, participation in social and leisure activities) and emotional well-being (loss of self-confidence, feelings of detachment, isolation). Daily pain was a paramount issue, and being pain- and fatigue-free was considered the main indicator of a "good day." Setting personal, social, and treatment goals, as well as monitoring disease progress to achieve these, was considered very beneficial by patients with RA, but discussion of treatment goals seldom appeared to be a part of medical appointments. CONCLUSION: Many patients with RA feel unable to communicate their disease burden and treatment goals, which are critically important to them, to their healthcare provider (HCP). Insights gained from these 2 surveys should help to guide patients and HCP to better focus upon mutually defined goals for continued improvement of management and achievement of optimal care in RA.
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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 | 0.006 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".