“It may help you to know…”: The Early-phase Qualitative Development of a Rheumatoid Arthritis Goal Elicitation Tool
Bibliographic record
Abstract
OBJECTIVE: Treatment guidelines for rheumatoid arthritis (RA) include a patient-centered approach and shared decision making, which includes a discussion of patient goals. We describe the iterative early development of a structured goal elicitation tool to facilitate goal communication for persons with RA and their clinicians. METHODS: Tool development occurred in 3 phases: (1) clinician feedback on the initial prototype during a communication training session; (2) semistructured interviews with RA patients; and (3) community stakeholder feedback on elements of the goal elicitation tool in a group setting and electronically. Feedback was dynamically incorporated into the tool. RESULTS: Clinicians (n = 15) and patients (n = 10) provided feedback on the tool prototypes. Clinicians preferred a shorter tool deemphasizing goals outside of their perceived treatment domain or available resources; they highlighted the benefits of the tool to facilitate conversation but raised concerns regarding current constraints of the clinic visit. Patients endorsed the utility of such a tool to support agenda setting and preparing for a visit. Clinicians, patients, and community stakeholders reported the tool was useful but identified barriers to implementation that the tool could itself resolve. CONCLUSION: A goal elicitation tool for persons with RA and their clinicians was iteratively developed with feedback from multiple stakeholders. The tool can provide a structured way to communicate patient goals within a clinic visit and help overcome reported barriers such as time constraints. Incorporating a structured communication tool to enhance goal communication and foster shared decision making may lead to improved outcomes and higher-quality 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 | 0.064 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".