Underserved groups could be better considered within population-based eye health surveys: a methodological study
Bibliographic record
Abstract
OBJECTIVES: In pursuit of health equity, the World Health Organization has recently called for more extensive monitoring of inequalities in eye health. Population-based eye health surveys can provide this information, but whether underserved groups are considered in the design, implementation, and reporting of surveys is unknown. We conducted a systematic methodological review of surveys published since 2000 to examine how many population-based eye health surveys have considered underserved groups in their design, implementation, or reporting. STUDY DESIGN AND SETTING: We identified all population-based cross-sectional surveys reporting the prevalence of objectively measured vision impairment or blindness. Using the PROGRESS + framework to identify underserved groups, we assessed whether each study considered underserved groups within 15 items across the rationale, sampling or recruitment methods, or the reporting of participation and prevalence rates. RESULTS: 388 eye health surveys were included in this review. Few studies prospectively considered underserved groups during study planning or implementation, for example within their sample size calculations (n = 5, ∼1%) or recruitment strategies (n = 70, 18%). The most common way that studies considered underserved groups was in the reporting of prevalence estimates (n = 374, 96%). We observed a modest increase in the number of distinct PROGRESS + factors considered by a publication over the study period. Gender/sex was considered within at least one item by 95% (n = 367) of studies. Forty-three percent (n = 166) of included studies were conducted primarily on underserved population groups, particularly for subnational studies of people living in rural areas, and we identified examples of robust population-based studies in socially excluded groups. CONCLUSION: More effort is needed to improve the design, implementation, and reporting of surveys to monitor inequality and promote equity in eye health. Ideally, national-level monitoring of vision impairment and service coverage would be supplemented with smaller-scale studies to understand the disparities experienced by the most underserved groups.
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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 | 0.437 | 0.575 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.016 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".