Building a Rheumatology Education Academy: Insights from Assessment of Needs During a Rheumatology Division Retreat
Bibliographic record
Abstract
OBJECTIVE: To implement a rheumatology department education retreat to systematically identify and address the key factors necessary to improve medical education in our division in preparation for developing a rheumatology academy. METHODS: The Hospital for Special Surgery organized a retreat for the Rheumatology Department aimed at (1) providing formal didactics and (2) assessing participants' self-reported skills and interest in education with the goal of directing this information toward formalizing improvement. In a mixed-methods study design, faculty and fellows in the Division of Rheumatology were surveyed online pre- and post-retreat regarding various aspects of the current education program, their teaching abilities, interest and time spent in teaching, divisional resources allocated, and how education is valued. RESULTS: Enthusiasm for teaching was high before and rose further after the retreat. Confidence in abilities was higher than expected before but fell afterward. Many noted that the lack of specific feedback on teaching skills and useful metrics to assess performance prevented the achievement of educational excellence. Most responding felt lack of time, knowledge of how to teach well, and resources prevented them from making greater commitments to educational endeavors and participating fully and effectively in the department's teaching activities. CONCLUSION: While most rheumatology faculty members want to improve as teachers, they know neither where their educational strengths and weaknesses lie nor where or how to begin to change their teaching abilities. The key elements for an academy would thus be an educational environment that elevates the quality of teaching throughout the division and promotes teaching careers and education research, and raises the importance and quality of teaching to equivalence with clinical care and research.
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 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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".