“It’s Like a One-Stop-Shop”: A Qualitative Study Exploring Patient Experiences With Interdisciplinary Team–Based Rheumatology Care
Bibliographic record
Abstract
OBJECTIVE: Interdisciplinary team-based models of rheumatology care, where rheumatologists and interdisciplinary healthcare professionals deliver care collaboratively, offer a promising solution to improve integrated care; however, an understanding of patients' experiences with this approach remains limited. We explored patients' perspectives on receiving care through an interdisciplinary team-based model. METHODS: This was a qualitative study informed by qualitative description. We conducted a secondary analysis of semistructured interviews with patients receiving care through an interdisciplinary model in Ontario, Canada. The interviews explored patients' experiences with team-based care and its perceived impact on disease management. We inductively coded interview transcripts and constructed themes using thematic analysis. RESULTS: Among 15 participants, 47% were female, 10 (67%) had inflammatory arthritis, 3 (20%) had other inflammatory rheumatic diseases, and 2 (13%) had osteoarthritis. We identified 5 overarching themes: (1) educational empowerment, (2) unhurried thoroughness, (3) responsive care, (4) timely care, and (5) personalized care through multispecialist collaboration. Participants perceived team-based care to offer enhanced access, including prompt appointments and timely responses to phone calls, attributed to the involvement of multiple health professionals. Participants described care as comprehensive and proactive, addressing needs beyond what a rheumatologist alone could provide. Longer consultations enabled thorough assessments, education, and support across all aspects of disease management. Participants valued the integrated "one-stop shop" model, which reduced the number of external referrals and separate appointments. CONCLUSION: Patients valued interdisciplinary team-based rheumatology care for improving care access and delivering integrated, convenient, comprehensive care that holistically addressed their needs. These results support wider implementation.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".