Physician resources and postgraduate training in canadian academic rheumatology centers: a 5-year prospective study.
Bibliographic record
Abstract
OBJECTIVE: To describe the trends in physician resources, changes in activity profiles, and the output of the postgraduate training programs in Canadian academic rheumatology centers from 1998-2002. METHODS: In 1998, the Canadian Council of Academic Rheumatologists (CCAR) established a prospective database to monitor physician resources, activity profiles, and recruitment within 15 academic rheumatology units in Canada. Information was also collected on residents pursuing subspeciality training in rheumatology. RESULTS: Over the 5 year period there was an increase in the number of rheumatologists from 157 to 168. The majority of this increase (91%) was attributable to changes in full-time staff. The mean age of rheumatologists increased from 47.9 to 48.9 years over the same period and the ratio of male to female rheumatologists decreased incrementally from 2.5:1 to 1.9:1. The overall allocation of time for clinical care (54-53%), teaching (17-16%), research (21-23%), and administration (7%) remained stable over time. Unfilled staff positions varied between 18-25 per year and were spread between 9-12 centers. The number of trainees in adult and pediatric rheumatology fell incrementally from 38 to 22 over the first 4 years of the study, with an increase to 30 in 2002. The majority of trainees were located at 2 centers and the number of active training programs varied between 6 and 12 per year. Funding for clinical fellowship training was provided by government (27-51%), the Arthritis Society (21-33%), and alternative sources (23-40%). CONCLUSION: These results indicate that rheumatology physician resources within Canadian academic units are inadequate to fulfill responsibilities in the delivery of clinical service and academic programs. Enrollment in rheumatology training programs is falling and is insufficient to meet the present and future needs for patients with rheumatic diseases in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".