Strategies to Improve Recruitment into Rheumatology: Results of the Workforce in Rheumatology Issues Study (WRIST)
Bibliographic record
Abstract
OBJECTIVE: By 2026, there will be a 64% shortfall of rheumatologists in Canada. A doubling of current rheumatology trainees is likely needed to match future needs; however, there are currently no evidence-based recommendations for how this can be achieved. The Workforce in Rheumatology Issues Study (WRIST) was designed to determine factors influencing the choice of rheumatology as a career. METHODS: An online survey was created and invitations to participate were sent to University of Western Ontario (UWO) medical students, UWO internal medicine (IM) residents, Canadian rheumatology fellows, and Canadian rheumatologists. Surveys sent to each group of respondents were identical except for questions related to demographics and past training. Participants rated factors that influenced their choice of residency and scored factors related to the attractiveness of rheumatology and to recruitment strategies. Statistical significance was determined using chi-squared and factor analysis. RESULTS: The survey went out to 1014 individuals, and 491 surveys were completed (48.4%). Responses indicated the importance of exposure through rotations and role models in considering rheumatology. Significant (p < 0.002) differences between groups were evident regarding what makes rheumatology attractive and effective recruitment strategies, most interestingly with rheumatologists and trainees expressing opposite views on the latter. CONCLUSION: Recommendations are made in 2 broad categories: greater exposure and greater information. As medical students and IM residents progress through their training, their interest in rheumatology lessens, thus it is important to begin recruitment initiatives as early as possible in the training process.
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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 | 0.039 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".