Ophthalmology training in sub-Saharan Africa: a scoping review
Bibliographic record
Abstract
Sub-Saharan Africa is home to 12% of the global population, and 4.3 million are blind and over 15 million are visually impaired. There are only 2.5 ophthalmologists per million people in SSA. Training of ophthalmologists is critical. We designed a systematic literature review protocol, searched MEDLINE Ovid and Embase OVID on 1 August 2019 and limited these searches to the year 2000 onwards. We also searched Google Scholar and websites of ophthalmic institutions for additional information. We include a total of 49 references in this review and used a narrative approach to synthesise the results. There are 56 training institutions for ophthalmologists in eleven Anglophone, eleven Francophone, and two Lusophone SSA countries. The median duration of ophthalmology training programmes was 4 years. Most curricula have been regionally standardised. National, regional and international collaborations are a key feature to ophthalmology training in more than half of ophthalmology training programmes. There is a drive, although perhaps not always evidence-based, for sub-specialisation in the region. Available published scientific data on ophthalmic medical and surgical training in SSA is sparse, especially for Francophone and Lusophone countries. However, through a broad scoping review strategy it has been possible to obtain a valuable and detailed view of ophthalmology training in SSA. Training of ophthalmologists is a complex and multi-faceted task. There are challenges in appropriate selection, capacity, and funding of available training institutions. Numerous learning outcomes demand curriculum, time, faculty, support, and appropriate assessment. There are opportunities provided by modern training approaches. Partnership is key.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.023 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".