A theory of change for patient-initiated follow-up care in rheumatoid arthritis
Bibliographic record
Abstract
BACKGROUND: Timely, high-quality care is critical to rheumatoid arthritis (RA) management. In Alberta, thousands of individuals with RA are waiting for care due to the resource-intensive nature of lifelong follow-ups and rheumatologist shortages. With 20-50% of routine follow-ups not leading to treatment changes or raising new concerns, many appointments may be avoidable if care were restructured. Patient-initiated models extend rheumatologist follow-up intervals beyond 12 months where appropriate, which can reduce inefficiencies and improve care access. To address provincial RA care challenges, we co-developed a theory of change (TOC) for patient-initiated follow-up care. METHODS: A TOC serves to define health services interventions and their intended impact prior to implementation testing. We worked with 35 healthcare leaders, implementation experts, and patient partners to co-develop a TOC for patient-initiated RA follow-up care. During the scoping phase, we held discussions with healthcare leaders and reviewed evidence on patient-initiated follow-up models to assess their implementation potential. During the development phase, we drafted a TOC map using scoping phase findings and clinical and patient expertise. During the refinement phase, feedback was collected to optimize the TOC. Meetings were recorded, transcribed, and analyzed using deductive qualitative content analysis alongside anonymous poll results and informal feedback to guide TOC refinement. RESULTS: The scoping phase identified challenges in RA care, including long waitlists and unnecessary appointments, which patient-initiated follow-up models have the potential to address. TOC discussions highlighted two intended impacts: (1) efficient and effective care for patients when needed, and (2) a sustainable model for RA care. Feedback in the refinement phase covered 4 topics: (1) preference for an interdisciplinary flare clinic, (2) patient selection, (3) patient education, and (4) patient monitoring. Tools and strategies were co-developed with partners to support patients (e.g., decision tool for patient-provider discussions) and the health system (e.g., monthly meetings to monitor burden). The final TOC for patient-initiated follow-up in RA details the care pathway, key resources and considerations, and evaluation outcomes. CONCLUSIONS: A patient-centered, context-specific patient-initiated RA follow-up care model was co-developed with patient and healthcare partners. An implementation pilot will test its ability to address RA care challenges. CLINICAL TRIAL REGISTRATION: Not applicable.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".