Patientsʼ Perception of COVID-19 Preventive Measures in Ophthalmology: Satisfaction and Impact on Glaucoma Care and Follow-up
Bibliographic record
Abstract
PURPOSE: In response to the spread of COVID-19 in Switzerland, ophthalmology practices implemented a variety of preventive measures in order to minimise the risk of contamination to their patients and staff. Yet, some studies suggested that over a quarter of all glaucoma patients never returned to the clinic after the first lockdown eased. This raises the question of the factors influencing Patients' likelihood to keep their appointments in this COVID-19 era. The aim of this study was therefore to assess ophthalmology Patients' perception of COVID-19 preventive measures, as well as their impact on glaucoma Patients' clinic attendance and follow-up. METHODS: This was a prospective study based on primary data collected from 12 private ophthalmology clinics in French-speaking Switzerland. A web-based patient experience questionnaire was designed to assess Patients' appreciation of the preventive measures in place at their eye care provider as well as their intention to attend further follow-up appointments. These measures were made on modified 10-point Likert scales. Every patient who attended an appointment at one of the participating clinics on randomly selected dates in October 2020 was offered voluntary enrolment into the study. RESULTS: In all, 118 surveys were completed and analysed. The mean age of respondents was 57.8 ± 18.0 years, 59.3% were female, and 71.2% were Swiss nationals. Fifty-four (45.8%) of them suffered from glaucoma. Overall, patients were highly satisfied with the measures in place to safeguard them from COVID-19 infection, with a mean score of 9.29 ± 1.35. This was higher amongst Swiss nationals (9.55 ± 0.77) than foreigners (8.65 ± 2.09). On average, responders expressed a 90.2 ± 17.5 percent likelihood to keep their follow-up appointment. The figure went down to 88.5 ± 19.9 percent amongst glaucoma patients (p = 0.58), and 86.3 ± 22.1 percent amongst glaucoma patients aged 65 and over (p = 0.29). Interestingly, older glaucoma Patients' satisfaction with preventive measures in place strongly correlated with their likelihood to keep follow-up appointments (r = 0.72). The correlation was moderate amongst all glaucoma patients (r = 0.46) and weak amongst all respondents (r = 0.38) and those over 65 (r = 0.44). CONCLUSIONS: The present research highlights the importance of Patients' perception on COVID-19 preventive measures in place in ophthalmology clinics, which was directly associated with their likelihood to keep follow-up appointments. Vulnerable subgroups of patients were more likely to miss their follow-up appointments altogether, and their decision to attend was more strongly influenced by their perceived risk of contracting COVID-19. This suggests a role for telemedicine in more vulnerable patients suffering from progressive diseases such as glaucoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".