Primary eye health services for older adults as a component of universal health coverage: a scoping review of evidence from high income countries
Bibliographic record
Abstract
In pursuit of Universal Health Coverage (UHC) for eye health, countries must strengthen services for older adults, who experience the highest prevalence of eye conditions. This scoping review narratively summarised (i) primary eye health services for older adults in eleven high-income countries/territories (from government websites), and (ii) the evidence that eye health services reduced vision impairment and/or provided UHC (access, quality, equity, or financial protection) (from a systematic literature search). We identified 76 services, commonly comprehensive eye examinations ± refractive error correction. Of 102 included publications reporting UHC outcomes, there was no evidence to support vision screening in the absence of follow-up care. Included studies tended to report the UHC dimensions of access ( n= 70), equity ( n= 47), and/or quality ( n= 39), and rarely reported financial protection ( n= 5). Insufficient access for population subgroups was common; several examples of horizontal and vertical integration of eye health services within the health system were described. Funding This work was funded by Blind Low Vision New Zealand for Eye Health Aotearoa.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".