Influence of presbyopia on smartphone usage among Chinese adults: A population study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
IMPORTANCE: Presbyopia, an essentially universal, age-related loss of the ability to focus un-aided on near objects, is the world's leading cause of visual impairment. BACKGROUND: Smartphone use is widespread in China, but little is known about the prevalence, determinants and correction of difficulties with smartphone use in the setting of presbyopia. DESIGN: Cross-sectional data from a population-based longitudinal cohort study. PARTICIPANTS: A total of 1817 persons aged ≥35 years in Guangzhou, Southern China. METHODS: Participants underwent near visual acuity (NVA) testing and completed questionnaires on smartphone usage detailing knowledge of their own presbyopia status, frequency (hours/day) and subjective difficulties with use of mobile and smartphones. Presbyopia was defined as uncorrected bilateral NVA ≤6/12 with best-corrected bilateral NVA >6/12. MAIN OUTCOME MEASURES: Difficulty in smartphone use associated with uncorrected presbyopia. RESULTS: Among 1427 respondents (78.5%) undergoing examination, 1191 (83.5%) completed questionnaires (mean age 52.3 ± 11.6 years; 54.9% women). Among 451 persons (37.8%) with presbyopia owning smartphones, 290 (64.3%) reported difficulty using them. Multiple ordinal logistic regression modelling showed difficulty in smartphone use due to presbyopia was associated with higher educational level (P = .013), worse NVA (P < .001) and more time spent using smartphones (P = .002 for 1-3 hours/day). Among persons with presbyopia owning smartphones, 353 (78.0%) said they would pay >US$15 (median US$45) for innovations making smartphone use easier. CONCLUSIONS AND RELEVANCE: Difficulty in using smartphones in the presence of presbyopia is common and affected persons are willing to pay for useful solutions to the problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.000 | 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 it