Implementation of eye screening programmes for patients with diabetes: a systematic map of evidence from five countries
Bibliographic record
Abstract
Background Diabetic retinopathy is a severe diabetes complication that can cause blindness. The United Kingdom’s pioneering diabetic eye screening programme has decreased blindness by early detection and treatment. Enhancing diabetic eye screening uptake requires a deeper understanding of the programme implementation. Objectives This study aimed to develop a logic model depicting diabetic eye screening programme implementation and to systematically map evidence on the implementation of diabetic eye screening in the United Kingdom and countries with similar health systems: Australia, Canada, Ireland and New Zealand. Methods A logic model was coproduced with UK National Screening Committee members and public coproducers with living experience of diabetic eye screening, informed by existing models and group knowledge. We searched 14 discipline-focused bibliographic databases, 3 academic search engines (Google Scholar, Bielefeld Academic Search Engine and OpenAlex) and targeted websites that covered the time frame up to December 2023. Eligible studies, from 2003 onwards, involved diabetic eye programme implementation in the target countries, covering a range of outcomes. Data extracted were publication year, study location (country), aim of study, study evaluation design, reported data (effectiveness outcomes, implementation outcomes, views/experiences data, observational data or data on resources required), study population, screening stage, intervention strategies and health inequality considerations. Findings are displayed as an interactive evidence map and searchable database. Results The coproduced logic model depicted factors that could be mapped: screening stage, intervention strategy and evidence type as well as ‘black box’ factors that would require an in-depth synthesis to address: points for improvement and mechanisms of action. One hundred and thirty-three records were included the interactive map. The largest subset of studies provided information relevant to the entire screening pathway or multiple parts of this system ( n = 85), followed by interventions relating to delivery of the eye screening appointment ( n = 36), while the fewest studies focused specifically on processes for identifying people eligible for screening. Few studies used experimental designs to evaluate the intervention effectiveness, and there were relatively few studies assessing how well interventions were implemented. Of the studies that reported the evaluation of some form of intervention, the most common type was environmental restructuring of the social and/or physical context ( n = 40). The most common data types were observational (e.g. audit studies; n = 69) and views or experiences ( n = 51). Most studies provided data that can contribute to tackling health inequalities ( n = 91). Limitations We identified 328 additional records that met the general inclusion criteria but were not included in the map for pragmatic reasons (e.g. the record only presented a conference abstract or brief report with limited detail about the study). Thus, the map reflects a subset of the evidence base. Also, the review’s focus on five countries may omit valuable insights from elsewhere. Conclusions A substantial body of evidence on diabetic eye programme implementation exists across five countries. However, evidence gaps remain, as certain process stages align with specific study types and data, highlighting areas for further research. The logic model and map may be useful for exploring ways to improve implementation of the programme. Future work Future evidence syntheses could analyse subsets of studies on health inequalities, implementation experiences and outcomes, quality assurance processes or the underlying mechanisms of interventions. Primary research could address the various gaps in the evidence base. Funding This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme as award number NIHR159996.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".