What Characteristics are Needed for Optimal Team-Based Rheumatology Care? A Qualitative Study Exploring the Experiences and Perceptions of Rheumatology Health Professionals
Bibliographic record
Abstract
Objectives The growth of the rheumatology workforce has been insufficient to meet the rising prevalence of rheumatic and musculoskeletal diseases (RMDs) and its increasingly complex management. Interdisciplinary teams, comprising health professionals from multiple disciplines with complementary skills,[1] offer promising solutions to rheumatology workforce shortages and enhancing patient-centered care. These team-based models hold potential for improving accessibility, quality, and equity in care for individuals with RMDs[2]; however, there remains a limited understanding of the optimal composition and structure of such teams. This study aimed to explore program and health professional characteristics that interdisciplinary health professionals (IHPs) perceived were necessary for optimal team-based care, informed by their experience practicing within a rheumatology team. Methods This was a qualitative descriptive study. We conducted a secondary analysis of semi-structured interviews with 11 IHPs and rheumatologists with experience working in an interdisciplinary rheumatology team in Ontario (Centre of Arthritis Excellence). Interviews were completed as part of an implementation research case study where participants were asked about their experiences working within an interdisciplinary team, and their perceptions of the factors necessary for optimal team function and for implementing this model of care at new sites. Interview transcripts were inductively coded (initially in duplicate) and thematically analyzed. Our multidisciplinary analytic team provided their diverse perspectives and ensured rigor by maintaining an interrogative approach to the data and keeping an audit trail. Results We constructed 3 themes: (1) Importance of program infrastructure; (2) Key IHP qualities (subthemes: rheumatology preparedness and the team player); and (3) Synergy of complementary skillsets (Figure 1). Participants emphasized the importance of sufficient infrastructure to support team functioning, particularly through shared workspaces, integrated electronic medical records, and competitive compensation. Rheumatology-specific training and experience were seen as critical to fully participate in interdisciplinary care. Team members’ attitudes, such as prioritizing trust, adaptability, and openness to feedback, were seen as crucial for effective teamwork. Participants also saw the value of using their complementary skillsets to enhance both patient care (perception of better clinical outcomes, higher care satisfaction, improved patient experience) and their own professional well-being. This synergy, in turn, fostered ongoing motivation for skill and attribute development among team members. Conclusion IHPs working within a rheumatology team viewed this model as beneficial for both patients and health professionals. Our findings suggest that providing IHPs with rheumatology-specific training, the appropriate clinic infrastructure, and having certain personal attributes could optimize team functioning and improve integrated care for RMDs. [1.] Nancarrow S. Hum Resour Health 2013;11:19. [2.] Barber C. J Rheumatol 2021;48:486-94.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".