Strategies for reducing visual impairment and blindness in rural and remote areas of Africa
Bibliographic record
Abstract
The prevalence of visual impairment (VI) and blindness in Africa is one of the highest in the world; a large proportion of the causes are preventable. The prevalence is particularly high in rural and remote areas, where many of the continent’s inhabitants live. This is of great concern because of the low number and poor distribution of primary eye care practitioners, as well as poor eye care infrastructure services in those areas. Uncorrected refractive errors are a major cause of avoidable VI and blindness, and optometrists play a major role in refractive error correction on the continent. However, as with other healthcare providers in Africa, optometrists are few and tend to be mainly in major cities. This paper highlights possible strategies, in alignment with the Ottawa Charter for Health Promotion, that can reduce VI in rural and remote areas of the continent. The strategies include increasing the eye care workforce, attracting them to rural areas and retaining them there, improving the eye care infrastructure, service improvement such as equitable distribution of eye care practitioners, implementing preventive measures such as vision screening and affordable spectacles, and eye health education such as eye health promotions, school health programmes and eye care awareness campaigns. Such strategies could drastically reduce the prevalence of VI and blindness in rural and remote areas of Africa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".