Barriers to and enablers of diabetic retinopathy screening attendance: a systematic review of published and grey literature
Bibliographic record
Abstract
AIMS: To identify and synthesize studies reporting modifiable barriers/enablers associated with retinopathy screening attendance in people with Type 1 or Type 2 diabetes, and to identify those most likely to influence attendance. METHODS: We searched MEDLINE, EMBASE, PsycINFO, Cochrane Library and the 'grey literature' for quantitative and qualitative studies to February 2017. Data (i.e. participant quotations, interpretive summaries, survey results) reporting barriers/enablers were extracted and deductively coded into domains from the Theoretical Domains Framework; with domains representing categories of theoretical barriers/enablers proposed to mediate behaviour change. Inductive thematic analysis was conducted within domains to describe the role each domain plays in facilitating or hindering screening attendance. Domains that were more frequently coded and for which more themes were generated were judged more likely to influence attendance. RESULTS: Sixty-nine primary studies were included. We identified six theoretical domains ['environmental context and resources' (75% of included studies), 'social influences' (51%), 'knowledge' (51%), 'memory, attention, decision processes' (50%), 'beliefs about consequences' (38%) and 'emotions' (33%)] as the key mediators of diabetic retinopathy screening attendance. Examples of barriers populating these domains included inaccurate diabetic registers and confusion between routine eye care and retinopathy screening. Recommendations by healthcare professionals and community-level media coverage acted as enablers. CONCLUSIONS: Across a variety of contexts, we found common barriers to and enablers of retinopathy screening that could be targeted in interventions aiming to increase screening attendance.
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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 | 0.027 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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".