Physicians’ Perceptions of Telemedicine Use During the COVID-19 Pandemic in Riyadh, Saudi Arabia: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: The term "telemedicine" refers to the use of communication technology to deliver health care remotely. The COVID-19 pandemic had substantial impacts on health care delivery from 2020 onward, and it was necessary to adapt high-quality care in a manner that limited the potential for viral exposure of both patients and health care workers. Physicians employed video, phone, and electronic written (e-consultation) visits, all of which provided quality of care comparable to that of face-to-face visits while reducing barriers of adopting telemedicine. OBJECTIVE: This study sought to assess physicians' perspectives and attitudes regarding the use of telemedicine in Riyadh hospitals during the COVID-19 pandemic. The main objects of assessment were as follows: (1) physicians' experience using telemedicine, (2) physicians' willingness to use telemedicine in the future, (3) physicians' perceptions of patient experiences, and (4) the influence of telemedicine on burnout. METHODS: This study employed SurveyMonkey to develop and distribute an anonymous 28-question cross-sectional survey among physicians across all specialty disciplines in Riyadh hospitals. A chi-square test was used to determine the level of association between variables, with significance set to P<.05. RESULTS: The survey was distributed among 500 physicians who experienced telemedicine between October 2021 and December 2021. A total of 362 doctors were included, of whom 28.7% (n=104) were consultants, 30.4% (n=110) were specialists, and 40.9% (n=148) were residents. Male doctors formed the majority 56.1% (n=203), and female doctors accounted for 43.9% (n=159). Overall, 34% (n=228) agreed or somewhat agreed that the "quality of care during telemedicine is comparable with that of face-to-face visits." Approximately 70% (n=254) believed that telemedicine consultation is cost-effective. Regarding burnout, 4.1% (n=15), 7.5% (n=27), and 27.3% (n=99) of the doctors reported feeling burnout every day, a few times a week, and a few times per month, respectively. CONCLUSIONS: The physicians had generally favorable attitudes toward telemedicine, believing that its quality of health care delivery is comparable to that of in-person care. However, further research is necessary to determine how physicians' attitudes toward telemedicine have changed since the pandemic and how this virtual technology can be used to improve physicians' professional and personal well-being.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".