How often and in what ways are underserved groups included in population-based eye health surveys? A methodological study
Bibliographic record
Abstract
Equity in health has risen in prominence in recent decades. Within eye health, The World Health Organization’s (WHO) World Report on Vision and the Lancet Global Health Commission on Global Eye Health both highlighted that in all parts of the world, there are population groups underserved by existing services, such as rural dwellers, women, Indigenous/First-Nations and non-dominant ethnicity groups, and people living in areas of high deprivation. These reports also called for more evidence and action to address inequity, including better monitoring of inequality. Population-based eye health surveys (including those employing the Rapid Assessment of Avoidable Blindness (RAAB) methodology) can be used by governments to strengthen eye health services to meet the needs of the population. These surveys assess and/or report the eye health needs of underserved groups in a range of ways, for example by intentionally recruiting communities with large unmet needs, or by conducting surveys in the general population and disaggregating the outcomes by different population groups. Future approaches to enhance inequality monitoring may include increasing the sample size so that it is adequately powered for subgroup analysis, adapting recruitment strategies to ensure they are appropriate for the target population groups, and finding ways to include traditionally ineligible population groups (e.g. people without housing / a fixed address). These modifications may allow surveys to be as equity-relevant as possible. We wish to identify the extent to which underserved population groups have been considered by researchers in the design, implementation, and reporting of population-based eye health surveys, and which strategies have been described. Our aims are to summarise: 1. The proportion of eye health surveys that have considered underserved groups in their design, implementation, and reporting; and 2. How and in what ways eye health surveys have considered underserved groups in their design, implementation, and reporting. In addition to identifying the range of strategies that have been implemented to date, the findings of this review will form a baseline from which the field can develop.
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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 | 0.619 | 0.760 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".