Impact of Dry Eye Symptoms and Daily Activities in a Modern Office
Bibliographic record
Abstract
PURPOSE: Modern offices and the use of electronic devices are increasing factors in work-related eye symptoms. However, symptoms of eye fatigue or dry eye sensation can be mixed and confusing. This study surveys the eye symptoms reported during a working day at modern offices to investigate the possible inhibition on daily work activities. METHODS: Two online digital surveys were sent to three different work locations, by direct e-mail. Survey A consisted of 14 questions that investigated eye symptoms experienced during daily activities at work and the impact on daily activities. Survey B consisted of four general questions, the Dutch Ocular Surface Disease Index, the Work Productivity and Activity Index, and the Illness Perception Questionnaire. RESULTS: A total of 505 participants completed survey A, and 213 completed survey B. The participants reported that a high proportion of their day was spent working on a computer (60%). The majority experienced an air draft (79.1%) and had no adjustable light (81.5%) at their workspace. Dry eye-related symptoms were reported at a significantly higher frequency at work than at home (P < .001). Up to 70% experienced some inhibition of daily activity at work due to eye symptoms, with more than 5% experiencing symptoms most or all of the time. Indoor environment, work environment, and general health were perceived as the main reasons for developing dry eye. Compared with males, females showed a statistically significant higher Ocular Surface Disease Index score (P < .001) and experienced more inhibition and adverse effects on daily life and work productivity. CONCLUSIONS: This investigation shows that dry eye symptoms have a negative impact on daily activities at work. These findings suggest that multidisciplinary understanding of the negative impact of dry eye by a range of specialists will be of help in managing work-related dry eye.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".