Physician Satisfaction With Virtual Ophthalmology Clinics During the COVID-19 Pandemic: A Tertiary Eye Care Center Experience
Bibliographic record
Abstract
Background In this study, we aimed to assess ophthalmologists' experience with teleophthalmology during the coronavirus disease 2019 (COVID-19) pandemic in the central region of Saudi Arabia. In addition, we evaluated their satisfaction level and explored their satisfaction determinants. Methodology We conducted an online survey for ophthalmologists who participated in the virtual ophthalmology clinic during COVID-19 between November 2020 and September 2021. The survey was used to evaluate ophthalmologists' experience with teleophthalmology during the pandemic. Ophthalmologists were asked to measure their satisfaction with equipment and technical issues, communication, and clinical assessment, and to provide an overall program evaluation. Data were analyzed via frequency measures (e.g., numbers, percentages, mean, and standard deviation). Results Out of the 113 ophthalmologists who were invited to participate in our study, 71 completed the survey. In total, 23 (32.4%) participants were general ophthalmologists, 15 (21.1%) were subspecialists in the cornea, 16 (22.5%) were subspecialists in glaucoma, one (1.4%) was a subspecialist in neuro-ophthalmology, seven (9.9%) were subspecialists in pediatric ophthalmology, eight (11.3%) were subspecialists in the retina, and one (1.4%) participant was a subspecialist in oculoplastic. Overall, 56.3% of the respondents were satisfied with teleophthalmology. Ophthalmologists who subspecialized in the retina demonstrated higher levels of satisfaction than other subspecialties. The most common challenge reported by ophthalmologists in the virtual consultation was the lack of adequate equipment to evaluate the patients (53.5%), followed by technical issues (43.7%) and the patients' lack of experience in using virtual consultation services (38%). Overall satisfaction score was the highest among ophthalmologists who reported providing at least five video consultations before the survey. Conclusions The findings from our study suggest that the subspeciality of ophthalmologists and the number of video consultations conducted by ophthalmologists are important determinants in their level of satisfaction with teleophthalmology. The majority of the respondents were satisfied with the virtual clinic during the COVID-19 pandemic. The current pandemic could pave the way for the future use of telemedicine in ophthalmology if virtual eye examinations become standardized.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".