Eye care delivery models to improve access to eye care for Indigenous people in high-income countries: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: Globally, there are an estimated 370 million Indigenous people across 90 countries. Indigenous people experience worse health compared with non-Indigenous people, including higher rates of avoidable visual impairment. Countries such as Australia and Canada have service delivery models aimed at improving access to eye care for Indigenous people. We will conduct a scoping review to identify and summarise these service delivery models to improve access to eye care for Indigenous people in high-income countries. METHODS AND ANALYSIS: An information specialist will conduct searches on MEDLINE, Embase and Global Health. All databases will be searched from their inception date with no language limits used. We will search the grey literature via websites of relevant government and service provider agencies. Field experts will be contacted to identify additional articles, and reference lists of relevant articles will be searched. All quantitative and qualitative study designs will be eligible if they describe a model of eye care service delivery aimed at Indigenous populations. Two reviewers will independently screen titles, abstracts and full-text articles; and complete data extraction. For each service delivery model, we will extract data on the context, inputs, outputs, Indigenous engagement and enabling health system functions. Where models were evaluated, we will extract details. We will summarise findings using descriptive statistics and thematic analysis. ETHICS AND DISSEMINATION: Ethical approval is not required, as our review will include published and publicly accessible data. This review is part of a project to improve access to eye care services for Māori in Aotearoa New Zealand. The findings will be useful to policymakers, health service managers and clinicians responsible for eye care services in New Zealand, and other high-income countries with Indigenous populations. We will publish our findings in a peer-reviewed journal and develop an accessible summary of results for website posting and stakeholder meetings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".