Knowledge Acquisition Related to Rheumatic and Musculoskeletal Diseases Among Advanced Clinician Practitioner in Arthritis Care Graduates: A Retrospective Review
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".