What Characteristics are Needed for Optimal Team-Based Rheumatology Care? A Qualitative Study Exploring the Experiences and Perceptions of Rheumatology Health Professionals
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,008 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».