The impact of COVID-19 on rheumatology training—results from the COVID-19 Global Rheumatology Alliance trainee survey
Bibliographic record
Abstract
Objective: The aim was to evaluate the impact of the coronavirus disease 2019 (COVID-19) pandemic on the clinical experiences, research opportunities and well-being of rheumatology trainees. Methods: A voluntary, anonymous, Web-based survey was administered in English, Spanish or French from 19 August 2020 to 5 October 2020. Adult and paediatric rheumatology trainees were invited to participate via social media and email. Using multiple-choice questions and Likert scales, the perceptions of trainees regarding the impact of the COVID-19 pandemic on patient care and redeployment, learning and supervision, research and well-being were assessed. Results: There were 302 respondents from 33 countries, with 83% in adult rheumatology training. An increase in non-rheumatology clinical work was reported by 45%, with 68% of these having been redeployed to COVID-19. Overall, trainees reported a negative impact on their learning opportunities during rheumatology training, including outpatient clinics (79%), inpatient consultations (59%), didactic teaching (55%), procedures (53%), teaching opportunities (52%) and ultrasonography (36%). Impacts on research experiences were reported by 46% of respondents, with 39% of these reporting that COVID-19 negatively affected their ability to continue their pre-pandemic research. Burnout and increases in stress were reported by 50% and 68%, respectively. Physical health was negatively impacted by training programme changes in 25% of respondents. Conclusion: The COVID-19 pandemic has had a substantial impact on rheumatology training and trainee well-being. Our study highlights the extent of this impact on research opportunities and clinical care, which are highly relevant to future curriculum planning and the clinical learning environment.
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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 | 0.006 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".