Evaluation of Rheumatology Workforce Supply Changes in Ontario, Canada, from 2000 to 2030
Bibliographic record
Abstract
Rheumatology workforces are increasingly challenged by too few physicians in face of the growing burden of rheumatic and musculoskeletal diseases (RMDs). Rheumatology is one of the most frequent non-surgical specialty referrals and has the longest wait times for subspecialists. We used a population-based approach to describe changes in the rheumatology workforce, patient volumes and geographic variation in the supply of and access to rheumatologists, in Ontario, Canada, between 2000 and 2019, and projected changes in supply by 2030. Over time, we observed greater feminization of the workforce and increasing age of workforce members. We identified a large regional variation in rheumatology supply. Fewer new patients are seen annually, which likely contributes to increasing wait times and reduced access to care. Strategies and policies to raise the critical mass and improve regional distribution of supply to effectively provide rheumatology care and support the healthcare delivery of patients with RMDs are needed.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".