Planning for the Rheumatologist Workforce
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate factors associated with rheumatologists' clinical work hours and patient volumes based on a national workforce survey in rheumatology. METHODS: Adult rheumatologists who participated in a 2015 workforce survey were included (n = 255). Univariate analysis evaluated the relationship between demographics (sex, age, academic vs. community practice, billing fee for service vs. other plan, years in practice, retirement plans) and workload (total hours and number of ½-day clinics per week) or patient volumes (number of new and follow-up consults per week). Multiple linear regression models were used to evaluate the relationship between practice type, sex, age, and working hours or clinical volumes. RESULTS: Male rheumatologists had more ½-day clinics (p = 0.05) and saw more new patients per week (p = 0.001) compared with females. Community rheumatologists had more ½-day clinics and new and follow-up visits per week (all p < 0.01). Fee-for-service rheumatologists reported more ½-day clinics per week (p < 0.001) and follow-ups (p = 0.04). Workload did not vary by age, years in practice, or retirement plans. In multivariate analysis, community practice remained independently associated with higher patient volumes and more clinics per week. Female rheumatologists reported fewer clinics and fewer follow-up patients per week than males, but this did not affect the duration of working hours or new consultations. Age was not associated with work volumes or hours. CONCLUSIONS: Practice type and rheumatologist sex should be considered when evaluating rheumatologist workforce needs, as the proportion of female rheumatologists has increased over time and alternative billing practices have been introduced in many centers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".