Rapid Adoption of Telemedicine in Rheumatology Care During the <scp>COVID</scp>‐19 Pandemic Highlights Training and Supervision Concerns Among Rheumatology Trainees
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of telemedicine use during the coronavirus disease 2019 (COVID-19) pandemic on rheumatology trainees. METHODS: A voluntary, anonymous, web-based survey was administered in English, Spanish, or French from August 19 to October 5, 2020. Adult and pediatric rheumatology trainees were invited to participate via social media and email. Using multiple-choice questions and Likert scales, the survey assessed prior and current telemedicine use, impact on training, and supervision after COVID-19 prompted rapid telemedicine implementation. RESULTS: Surveys were received from 302 trainees from 33 countries, with 83% in adult rheumatology training programs. Reported telemedicine use increased from 13% before the pandemic to 82% during the pandemic. United States trainees predominantly used video visits, whereas outside the United States telemedicine was predominantly audio only. Most (65%) evaluated new patients using telemedicine. More respondents were comfortable using telemedicine for follow-up patients (69%) than for new patients (25%). Only 39% of respondents reported receiving telemedicine-focused training, including instruction on software, clinical skills, and billing, whereas more than half of United States trainees (59%) had training. Postconsultation verbal discussion was the most frequent form of supervision; 24% reported no supervision. Trainees found that telemedicine negatively impacted supervision (50%) and the quality of clinical teaching received (70%), with only 9% reporting a positive impact. CONCLUSIONS: Despite widespread uptake of telemedicine, a low proportion of trainees received telemedicine training, and many lacked comfort in evaluating patients, particularly new patients. Inadequate supervision and clinical teaching were areas of concern. If telemedicine remains in widespread use, ensuring appropriate trainee supervision and teaching should be prioritized.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".