The Relationship Between Insufficient Vision and Technology Access and Use Among Hospitalized Adults at an Urban Academic Hospital: Observational Study
Bibliographic record
Abstract
BACKGROUND: The role of sufficient vision in self-management is salient with respect to the growing prevalence of eHealth-based interventions for chronic diseases. However, the relationship between insufficient vision and self-management has been understudied. OBJECTIVE: We aimed to assess differences in access to and use of technology among adults with and without insufficient vision at an academic urban hospital. METHODS: This is an observational study of hospitalized adult general medicine patients that is part of a larger quality improvement study called the hospitalist study. The hospitalist study provided demographic and health literacy data (Brief Health Literacy Screen). Our substudy included several measures. Validated surveys assessed technology access and use, and included benchmarked questions from the National Pew Survey to determine access to, willingness to use, and self-described ability to use technology at home, particularly for self-management, and eHealth-specific questions assessing future willingness to access eHealth post discharge. The eHealth Literacy Scale (eHEALS) was used to assess eHealth literacy. Visual acuity was assessed using the Snellen pocket eye chart with low vision defined as visual acuity ≤20/50 in at least one eye. Descriptive statistics, bivariate chi-square analyses, and multivariate logistic regressions (adjusted for age, race, gender, education level, and eHealth literacy) were performed using Stata. RESULTS: A total of 59 participants completed our substudy. The mean age was 54 (SD 16.4) years. Demographic data from the hospitalist study was missing for several participants. Among those who responded, most identified as Black (n=34, 79%) and female (n=26, 57%), and most reported at least some college education (n=30, 67%). Most participants owned technology devices (n=57, 97%) and had previously used the internet (n=52, 86%), with no significant differences between those with insufficient and sufficient vision (n=34 vs n=25). Though there was a 2x effect size for laptop ownership, with those with sufficient vision more likely to own a laptop, those with insufficient vision versus sufficient vision were less likely to report an ability to perform online tasks without assistance, including using a search engine (n=22, 65% vs n=23, 92%; P=.02), opening an attachment (n=17, 50% vs n=22, 88%; P=.002), and using an online video (n=20, 59% vs n=22, 88%; P=.01). In multivariate analysis, the ability to independently open an online attachment did not remain statistically significant (P=.01). CONCLUSIONS: Technology device ownership and internet use rates are high in this population, yet participants with insufficient vision (vs sufficient vision) reported a reduced ability to independently perform online tasks. To ensure the effective use of eHealth technologies by at-risk populations, the relationship between vision and technology use needs to be further studied.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".