Measuring the Rheumatology Workforce in Canada: A Literature Review
Bibliographic record
Abstract
OBJECTIVE: The number of rheumatologists per capita has been proposed as a performance measure for arthritis care. This study reviews what is known about the rheumatologist workforce in Canada. METHODS: A systematic search was conducted in EMBASE and MEDLINE using the search themes "rheumatology" AND "workforce" AND "Canada" from 2000 until December 2014. Additionally, workforce databases and rheumatology websites were searched. Data were abstracted on the numbers of rheumatologists, demographics, retirement projections, and barriers to healthcare. RESULTS: Twenty-five sources for rheumatology workforce information were found: 6 surveys, 14 databases, 2 patient/provider resources, and 3 epidemiologic studies. Recent estimates say there are 398 to 428 rheumatologists in Canada, but there were limited data on allocation of time to clinical practice. Although the net number of rheumatologists has increased, the mean age was ≥ 47.7 years, and up to one-third are planning to retire in the next decade. There is a clustering of rheumatologists around academic centers, while some provinces/territories have suboptimal ratios of rheumatologists per capita (range 0-1.1). Limited information was found on whether rural areas are receiving adequate services. The most consistent barrier reported by rheumatologists was lack of allied health professionals. CONCLUSION: In Canada there are regional disparities in access to rheumatologist care and an aging rheumatologist workforce. To address these workforce capacity issues, better data are needed including information on clinical full-time equivalents, delivery of care to remote communities, and use of alternative models of care to increase clinical capacity.
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.
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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.038 | 0.063 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".