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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".