The Challenge of Addressing the Rheumatology Workforce Shortage
Bibliographic record
Abstract
The rheumatology workforce faces a deficit of physicians trained to provide high-quality care to patients with rheumatic diseases, and this deficit is projected to worsen over the next 10 to 15 years in many countries and regions around the world. Rheumatology workforce studies carried out in the US, Canada, and in Europe have revealed expected shortages driven by projections for increased demand; changes in demographics among providers, including increasing proportions of women and part-time clinicians as well as high levels of expected retirements; and geographic maldistribution of providers.1,2,3,4,5 In the last 2 years, practice changes caused by the coronavirus disease 2019 (COVID-19) pandemic have affected, and likely exacerbated, workforce limitations. In this issue of The Journal of Rheumatology , Kulhawy-Wibe and colleagues report on the results of the Canadian Rheumatology Association’s Workforce and Wellness survey.6 The study highlights some of the known threats of burnout to the workforce as well as newer challenges related to the pandemic. Similar to findings among US rheumatologists, more than half of respondents in this study reported burnout, especially among younger (millennial) rheumatologists and among women.7 This finding is particularly notable since both categories are increasingly proportional to the total workforce, tend to see fewer patients on average, and are crucial to the future rheumatology workforce. Drivers of burnout including work-related stress and workload, loss of control and meaning, inefficiency, and the challenges of electronic health records (EHRs) are familiar from prior studies addressing burnout among a variety … Address correspondence to Dr. E. Miloslavsky, Massachusetts General Hospital, Yawkey Center for Outpatient Care, 55 Fruit St., Boston, MA 02114, USA. Email: emiloslavsky{at}mgh.harvard.edu.
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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 | 0.023 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.029 | 0.013 |
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".