Global eye care frameworks: a review of strategies, gaps, and recommendations for equitable access
Bibliographic record
Abstract
Access to eye care remains a global health priority, particularly for underserved populations in rural, Indigenous, and low-income communities. Despite advancements in ophthalmic care and technology, substantial inequities persist, exacerbating preventable vision loss and its associated social and economic consequences. International and national eye care frameworks have emerged as critical tools to address these disparities by guiding policy, resource allocation, and service delivery. This narrative review synthesizes strategies, outcomes, and gaps from 14 international and national frameworks globally. Measurable outcomes also are reported to illustrate the implementation impact. Key strategies identified across these frameworks include the integration of eye care into primary health care and universal health coverage, attention to workforce adequacy and distribution including workforce development through training primary care providers and community health workers, early detection programs, use of technology such as tele-ophthalmology and artificial intelligence, and community engagement through culturally sensitive outreach. However, notable gaps remain, including limited implementation tools, inconsistent success metrics, uneven workforce distribution, inadequate funding, and barriers to technological adoption, particularly in low-resource settings. By highlighting both effective strategies and persistent challenges, this study provides actionable insights for policymakers and stakeholders seeking to develop or enhance national eye care frameworks. A coordinated, equity-focused approach is essential to reduce preventable vision loss and improve health outcomes worldwide.
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 | 0.039 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".