Cataract services for all: Strategies for equitable access from a global modified Delphi process
Bibliographic record
Abstract
Vision loss from cataract is unequally distributed, and there is very little evidence on how to overcome this inequity. This project aimed to engage multiple stakeholder groups to identify and prioritise (1) delivery strategies that improve access to cataract services for under-served groups and (2) population groups to target with these strategies across world regions. We recruited panellists knowledgeable about cataract services from eight world regions to complete a two-round online modified Delphi process. In Round 1, panellists answered open-ended questions about strategies to improve access to screening and surgery for cataract, and which population groups to target with these strategies. In Round 2, panellists ranked the strategies and groups to arrive at the final lists regionally and globally. 183 people completed both rounds (46% women). In total, 22 distinct population groups were identified. At the global level the priority groups for improving access to cataract services were people in rural/remote areas, with low socioeconomic status and low social support. South Asia and Sub-Saharan Africa were the only regions in which panellists ranked women in the top 5 priority groups. Panellists identified 16 and 19 discreet strategies to improve access to screening and surgical services, respectively. These mostly addressed health system/supply side factors, including policy, human resources, financing and service delivery. We believe these results can serve eye health decision-makers, researchers and funders as a starting point for coordinated action to improve access to cataract services, particularly among population groups who have historically been left behind.
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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 | 0.234 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.037 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".