Telemedicine Use during the COVID-19 Pandemic: Results of an International Survey
Bibliographic record
Abstract
Abstract Objective The aim of the study is to survey hand surgeons' perspectives on telemedicine during the coronavirus disease 2019 (COVID-19) pandemic and intended applications after the pandemic. Methods Online surveys were sent to 285 Canadian and American surgeons in late April and early May 2020. Results Response rate was 63% (180)—84% (152) American and 16% (28) Canadian. Forty-three percent (76) of respondents were in private practice, 36% (64) academics, 13% (24) privademics, and 6% (12) hospital employed. The most common telemedicine platform was Zoom. During the pandemic, 42% of patient visits were conducted via telemedicine; however, 37% required a subsequent in-person office visit. The most common complaint by surgeons was the inability to provide routine in-office procedures. The most beneficial feature was ease of use, and the most frustrating feature was connectivity difficulty. Time spent was similar to in-person visits, and surgeons were likely to recommend their platforms. Surgeons were neutral about using telehealth in the future and were most likely to use it for follow-up visits. New patient visits for traumatic injuries or fractures were of limited value. Canadians used telemedicine for a greater proportion than Americans (50 vs. 40%, p <0.05) and spent more time than in-person visits (7/10 vs. 5/10, p <0.05). Americans were more likely to use telemedicine for postoperative follow-up visits (6/10 vs. 4/10, p <0.05) and in mornings before clinic opens (4/10 vs. 2/10, p <0.05). Private practices were more likely to use telemedicine for future allied health provider visits than all other practice types (p <0.05). Conclusion Telemedicine comprised nearly half of patient encounters during the COVID-19 pandemic, but limitations remain.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".