Attracting Internal Medicine Trainees to Rheumatology: Where and When Programs Should Focus Efforts
Bibliographic record
Abstract
OBJECTIVE: To determine where and when efforts should be focused to increase recruitment of rheumatology trainees from internal medicine (IM) programs. METHODS: (1) We calculated the percentage of trainees at each of the 13 English-speaking Canadian IM-accredited programs who entered a rheumatology training program in Canada from 2005 to 2007. We then correlated this with the opportunity they would have had to do a rheumatology rotation in each of their 3 postgraduate years of IM training. (2) We calculated the overall percentage of residents who remained at the same training institution after their IM program, 2005-2007, comparing this to 4 similar-size subspecialty training programs. RESULTS: Among IM trainees, 3.5% began rheumatology training in Canada. There was a positive relationship at the postgraduate year 1 (PGY1) level between more rheumatology opportunities and chance of entering rheumatology (r(2) = 0.35, p < 0.05), but not at the PGY2 or PGY3 level. Among rheumatology trainees, 78% remained at the training institution where they completed IM training, more than the 70% of gastroenterology trainees, 68% of nephrology trainees, 67% of endocrinology trainees, and 76% of infectious diseases trainees. CONCLUSION: The opportunity for a rheumatology rotation in the first year of IM training increases the likelihood the trainee may choose rheumatology as a career. Further, most rheumatology trainees continue at the same institution as their IM training, more than other subspecialties. This information may assist recruitment efforts to increase numbers of rheumatology trainees and the overall rheumatology workforce. These data warrant reevaluation of IM programs of study in order to influence trainee career choices and plan better for future workforce requirements in all IM fields.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".