Knowledge Acquisition Related to Rheumatic and Musculoskeletal Diseases Among Advanced Clinician Practitioner in Arthritis Care Graduates: A Retrospective Review
Notice bibliographique
Résumé
Objectives The Advanced Clinician Practitioner in Arthritis Care (ACPAC) Program is a post-licensure competency-based academic program that educates experienced physiotherapists, occupational therapists, nurses, and chiropractors, in the advanced assessment and management of rheumatic and musculoskeletal diseases (RMDs). Since the inception of the ACPAC program in 2005, the curricular content has evolved, based on current evidence[1] and changing health system needs.[2] The purpose of this study was to determine overall knowledge acquisition of ACPAC program learners from 2022 to 2024 with respect to core clinical competencies and disease categories for RMD practice. Methods This retrospective study evaluated pre-program and post-program written examination scores for ACPAC program graduation cohorts 2022-23 and 2023-24. Paired clinical case-based written examination scores were obtained through the Office of Continuing Professional Development, Temerty Faculty of Medicine, University of Toronto. Scores reflected the curriculum competencies including pathological features of RMD; differential diagnosis of RMD; investigation interpretation (laboratory and imaging), pharmacotherapy, triage and management, and disease category. Wilcoxon signed-rank tests were used to determine change in pre-program test scores to post-program test scores. Results Over the 2-year study period, 21 learners (17 physiotherapists; 3 occupational therapists; and 1 chiropractor) graduated from the ACPAC program. On average they had 14.8 years’ post-licensure experience in their respective professions. Geographical distribution was as follows: Central/Southern Ontario (n=17); Northern Ontario (n=2); Quebec (n=1); and Ireland (n=1). We calculated pre- and post-program paired scores across a number of competencies. Overall knowledge improvement from beginning to end of program based on written scores was 35.3% (p<0.001). Significant improvement in knowledge for clinical competencies was also demonstrated including pathological features of RMD: 34.2% (p<0.001); differential diagnosis of RMD: 43.2% (p<0.001); imaging interpretation: 32.6% (p<0.001); laboratory interpretation: 29.0% (p<0.001); pharmacology: 37.6% (p<0.001); and triage/management: 32.6% (p<0.001). Knowledge acquisition related to disease categorization revealed adult inflammatory arthritis: 34.0% (p<0.001); systemic autoimmune rheumatic diseases: 33.2% (p<0.001); pediatric musculoskeletal: 31.0% (p<0.001); monoarthritis: 38.2% (p<0.001); and orthopedic: 60.2% (p<0.001). Conclusion Knowledge acquisition, related to RMDs, among experienced clinicians enrolled in the ACPAC program was significant across all competencies and disease categories addressed in the rigorous competency-based curriculum. With measured changes in overall knowledge, specific curricular competencies, including acquired knowledge for triage and management, ACPAC program graduates have the potential to address emerging unmet RMD health system needs by adopting extended scope roles.[3] [1.] Alharbi N. BMC Med Educ 2024;24:612. [2.] Ahluwalia V. J Multidiscip Healthc 2021;14:1299-310. [3.] Passalent L. Healthcare Policy 2013;8:56-70.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».