Eye care delivery models to improve access to eye care for Indigenous peoples in high-income countries: a scoping review
Bibliographic record
Abstract
PURPOSE: Globally, there are ~370 million Indigenous peoples. Indigenous peoples typically experience worse health compared with non-Indigenous people, including higher rates of avoidable vision impairment. Much of this gap in eye health can be attributed to barriers that impede access to eye care services. We conducted a scoping review to identify and summarise service delivery models designed to improve access to eye care for Indigenous peoples in high-income countries. METHODS: Searches were conducted on MEDLINE, Embase and Global Health in January 2019 and updated in July 2020. All study designs were eligible if they described a model of eye care service delivery aimed at populations with over 50% Indigenous peoples. Two reviewers independently screened titles, abstracts and full-text articles and completed data charting. We extracted data on publication details, study context, service delivery interventions, outcomes and evaluations, engagement with Indigenous peoples and access dimensions targeted. We summarised findings descriptively following thematic analysis. RESULTS: We screened 2604 abstracts and 67 studies fulfilled our eligibility criteria. Studies were focused on Indigenous peoples in Australia (n=45), USA (n=11), Canada (n=7), New Zealand (n=2), Taiwan (n=1) and Greenland (n=1). The main disease focus was diabetic retinopathy (n=30, 45%), followed by 'all eye care' (n=16, 24%). Most studies focused on targeted interventions to increase availability of services. Fewer than one-third of studies reported involving Indigenous communities when designing the service. 41 studies reflected on whether the model improved access, but none undertook rigorous evaluation or quantitative assessment. CONCLUSIONS: The geographical and clinical scope of service delivery models to improve access to eye care for Indigenous peoples in high-income countries is narrow, with most studies focused on Australia and services for diabetic retinopathy. More and better engagement with Indigenous communities is required to design and implement accessible eye care services.
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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 | 0.021 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".