Telemedicine Use During the COVID-19 Pandemic by Resilient Rheumatology Providers: A National Veterans Affairs Follow-up Survey
Bibliographic record
Abstract
Objective To assess rheumatology provider experience and practices at Veterans Affairs (VA) facilities during the coronavirus disease 2019 (COVID-19) pandemic. Methods We performed an anonymized follow-up national cross-sectional survey (November 5, 2020 to January 1, 2021) to assess provider resilience, experience, practices, views, and opinions about changes to medications and laboratory monitoring of veterans with rheumatic diseases. Results Of the 143 eligible VA rheumatology providers, 114 (80%) responded. Compared to the original survey, fewer providers reported using telephone visits (78% vs 91%, P = 0.009), and more used clinical video telehealth (CVT; 16% vs 7%, P = 0.04) or in-person visits (76% vs 59%, P = 0.007). Most providers were somewhat or very comfortable with the quality of clinical encounters for established but not new patients for telephone, video-based VA Video Connect (VVC), and CVT. The mean 2-item Connor-Davidson Resilience Scale score was 6.85 (SD 1.06, range 0–8), significantly higher than the original April–May 2020 survey score of 6.35 (SD 1.26; P = 0.004). When adjusted for age, sex, and ethnicity, high provider resilience was associated with significantly higher odds of comfort with technology and the quality of the VVC visit for the following: (1) established patients (odds ratio [OR] 1.72, 95% CI, 0.67–4.40 and OR 4.13, 95% CI 1.49–11.44, respectively) and (2) new patients (OR 2.79, 95% CI 1.11–7.05, and OR 2.69, 95% CI 1.06–6.82, respectively). Conclusion Reassuringly, VA rheumatology providers became increasingly comfortable with video visits during the first 10 months of the COVID-19 pandemic. High provider resilience, and its association with better quality CVTs, raise the possibility that video visits might be an acceptable substitute for in-person visits under appropriate circumstances.
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 | 0.001 | 0.003 |
| 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) | 0.002 | 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".