Stand Up and Be Counted: Measuring and Mapping the Rheumatology Workforce in Canada
Bibliographic record
Abstract
OBJECTIVE: To characterize the practicing rheumatologist workforce, the Canadian Rheumatology Association (CRA) launched the Stand Up and Be Counted workforce survey in 2015. METHODS: The survey was distributed electronically to 695 individuals, of whom 519 were expected to be practicing rheumatologists. Demographic and practice information were elicited. We estimated the number of full-time equivalent rheumatologists per 75,000 population from the median proportion of time devoted to clinical practice multiplied by provincial rheumatologist numbers from the Canadian Medical Association. RESULTS: The response rate was 68% (355/519) of expected practicing rheumatologists (304 were in adult practice, and 51 pediatric). The median age was 50 years, and one-third planned to retire within the next 5-10 years. The majority (81%) were university-affiliated. Rheumatologists spent a median of 70% of their time in clinical practice, holding 6 half-day clinics weekly, with 10 new consultations and 45 followups seen per week. Work characteristics varied by type of rheumatologist (adult or pediatric) and by practice setting (community- or university-based). We estimated between 0 and 0.8 full-time rheumatologists per 75,000 population in each province. This represents a deficit of 1 to 77 full-time rheumatologists per province/territory to meet the CRA recommendation of 1 rheumatologist per 75,000 population, depending on the province/territory. CONCLUSION: Our results highlight a current shortage of rheumatologists in Canada that may worsen in the next 10 years because one-third of the workforce plans to retire. Efforts to encourage trainees to enter rheumatology and strategies to support retention are critical to address the shortage.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".