Measuring Advanced/Extended Practice Roles in Arthritis and Musculoskeletal Care in Canada
Bibliographic record
Abstract
OBJECTIVE: Our objective was to characterize Canadian workforce attributes of extended role practitioners (ERPs) in arthritis care. METHODS: We used an exploratory, mixed-methods study that was based on the Canadian Rheumatology Association's Stand Up and Be Counted Rheumatologist Workforce Survey (2015). An anonymous online survey was deployed to groups of non-physician health care professionals across Canada who potentially had post-licensure training in arthritis care. Demographic and practice information were elicited. Qualitative responses were analyzed using grounded theory techniques. RESULTS: Of 141 respondents, 91 identified as practicing in extended role capacities. The mean age of ERP respondents was 48.7; 87% were female, and 41% of ERPs planned to retire within 5 to 10 years. Respondents were largely physical or occupational therapists by profession and practiced in urban/academic (46%), community (39%), and rural settings (13%). Differences in practice patterns were noted between ERPs (64.5%) and non-ERPs (34.5%), with more ERPs working in extended role capacities while retaining activities reflective of their professional backgrounds. Most respondents (95%) agreed that formal training is necessary to work as an ERP, but only half perceived they had sufficient training opportunities. Barriers to pursuing training were varied, including personal barriers, geographic barriers, patient-care needs, and financial/remuneration concerns. CONCLUSION: To our knowledge, no previous studies have assessed the workforce capacity or the perceived need for the training of ERPs working in arthritis and musculoskeletal care. Measurement is important because in these health disciplines, practitioners' scopes of practice evolve, and ERPs integrate into the Canadian health care system. ERPs have emerged to augment provision of arthritis care, but funding for continuing professional development opportunities and for role implementation remains tenuous.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".