The Association of Rheumatologist Supply and Multidisciplinary Care With Timely Patient Access to Rheumatologists: Evidence From British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVE: The objective was to understand how the expansion of rheumatology supply and the introduction of multidisciplinary care was associated with access to rheumatology services. METHODS: We accessed Population Data BC, a longitudinal database with de-identified individual-level health data on all residents of British Columbia, Canada, to analyze physician visits and prescribing from 2010-2011 to 2019-2020. We calculated access as the time from referral to first rheumatologist visit and, for people with rheumatoid arthritis (RA), time to first disease-modifying antirheumatic drug (DMARD). Associations between lag time, patient characteristics, and system variables were explored using quantile regression. RESULTS: Over the study period, there were 149,902 new rheumatologist visits, with 31% more visits in 2019-2020 than in 2010-2011. The proportion of first visits for patients with inflammatory arthritis increased from 28% to 51%. The median time from referral to first visit decreased by 22 days (35%) from 63 days (interquartile range 21-120 days) in 2010-2011. For people with RA, time from referral to DMARD decreased by 4 days (6%) to 62 days. Male sex, living in metropolitan areas, and having a rheumatologist who used a multidisciplinary care assessment code were associated with shorter times from referral to first DMARD. CONCLUSION: Access to rheumatology care improved, and the increased proportion of patients with IA in the first visits case-mix indicates that rheumatologist supply and incentives for multidisciplinary care may have improved referral patterns. However, time to DMARDs for people with RA remained long, and we found signals of unequal access for female patients and people living outside of metropolitan areas.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".