Disparities in Vision-Related Functional Impairments Among Adults in the United States
Bibliographic record
Abstract
PURPOSE: To investigate the relationships between vision-related functional impairment (VFI) with sociodemographic and healthcare access factors in a representative sample of the United States population. METHODS: Data from the 2017 National Health Interview Survey (NHIS) were used. The NHIS involves responses from the U.S. civilian, non-institutionalized population aged 18 years or older. It provides self-reported data on demographic characteristics, socioeconomic factors, health status, and healthcare access. NHIS participants who responded to at least one of our target questions about VFI were included in the study. VFI was defined for participants based on their 'yes' or 'no' responses to target questions about experiencing a VFI. Data analysis was performed through univariable and multivariable logistic regression. RESULTS: < 0.001) had higher odds of VFI than those with income >5× poverty threshold. CONCLUSIONS: Several demographic and economic factors are associated with VFI in a representative sample of the U.S. population. These results highlight the importance of addressing social and economic factors that are associated with the development of VFI.
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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".