How to Attract Trainees, a Pan-Canadian Perspective: Phase 1 of the “Training the Rheumatologists of Tomorrow” Project
Bibliographic record
Abstract
OBJECTIVE: To identify what learners and professionals associated with rheumatology programs across Canada recommend as ways to attract future trainees. METHODS: Data from online surveys and individual interviews with participants from 9 rheumatology programs were analyzed using the thematic framework analysis to identify messages and methods to interest potential trainees in rheumatology. RESULTS: There were 103 participants (78 surveyed, 25 interviewed) who indicated that many practitioners were drawn to rheumatology because of the aspects of work life, and that educational events and hands-on experiences can interest students. Messages centered on working life, career opportunities, and the lifestyle of rheumatologists. Specific ways to increase awareness about rheumatology included information about practice type, intellectual and diagnostic challenges, diversity of diseases, and patient populations. Increased opportunity for early and continued exposure for both medical students and internal medicine residents was also important, as was highlighting job flexibility and availability and a good work-life balance. Although mentors were rarely mentioned, many participants indicated educational activities of role models. The relatively low pay scale of rheumatologists was rarely identified as a barrier to choosing a career in rheumatology. CONCLUSION: This is the first pan-Canadian initiative using local data to create a work plan for developing and evaluating tools to promote interest in rheumatology that could help increase the number of future practitioners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".