“How are you?” Perspectives From Patients and Health Care Providers of Text Messaging to Support Rheumatoid Arthritis Care: A Thematic Analysis
Bibliographic record
Abstract
OBJECTIVE: Patients with rheumatoid arthritis (RA) may need to access rheumatology care between scheduled visits. WelTel is a virtual care platform that supports secure two-way text-messaging between patients and their health care team. The objective of the present study was to explore perspectives and experiences of health care providers (HCPs) and patients related to the use of WelTel as an adjunct to routine care. METHODS: Seventy patients with RA were enrolled in a six-month WelTel pilot project launched in September 2021. Patients received monthly "How are you?" text message check-ins and could message their health care team during clinic hours to request health advice. The current project is a qualitative study of the WelTel pilot. A subgroup of pilot participants was purposively sampled and invited to participate in interviews. A thematic analysis of transcripts was conducted using a deductive approach leveraging quality of care domains. RESULTS: Thirteen patients (62% female, mean age 62 years, 10 White) completed interviews. Patients' views suggested that text messaging with the rheumatology team supported high-quality care across multiple quality domains including patient-centeredness, timeliness, efficiency, safety, effectiveness, equity, and appropriateness. Seven HCPs (57.1% female, one pharmacist and six rheumatologists) completed interviews. HCPs' perspectives varied based on their experience with the WelTel platform. Additional themes reported by HCPs included perceived increased workload and burnout. CONCLUSIONS: Patients with RA perceived text-based messaging as supporting high-quality care. The impact of increased communications on HCP burnout and workload requires consideration, and future studies should evaluate the effect of texting on patient outcomes.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".