Encounters with Rheumatologists in a Publicly Funded Canadian Healthcare System: A Population-based Study
Bibliographic record
Abstract
OBJECTIVE: To quantify population-level and practice-level encounters with rheumatologists over time. METHODS: We conducted a population-based study from 2000 to 2015 in Ontario, Canada, where all residents are covered by a single-payer healthcare system. Annual total number of unique patients seen by rheumatologists, the number of new patients seen, and total number of encounters with rheumatologists were identified. RESULTS: From 2000 to 2015, the percentage of the population seen by rheumatologists was constant over time (2.7%). During this time, Ontario had a stable supply of rheumatologists (0.8 full-time equivalents/75,000). From 2000 to 2015, the number of annual rheumatology encounters increased from 561,452 to 786,061, but the adjusted encounter rates remained stable over time (at 62 encounters per 1000 population). New patient assessment rates declined over time from 10 new outpatient assessments per 1000 in 2000 to 6 per 1000 in 2015. The crude volume of new patients seen annually decreased and an increasing proportion of rheumatology encounters were with established patients. We observed a shift in patient case mix over time, with more assessments for systemic inflammatory conditions. Rheumatologists' practice volumes, practice sizes, and the annual number of days providing clinical care decreased over time. CONCLUSION: Over a 15-year period, the annual percentage of the population seen by a rheumatologist remained constant and the volume of new patients decreased, while followup patient encounters increased. Patient encounters per rheumatologist decreased over time. Our findings provide novel information for rheumatology workforce planning. Factors affecting clinical activity warrant further research.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".