Stand Up and Be Counted: Measuring and Mapping the Rheumatology Workforce in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it