Barriers and enablers to diabetic retinopathy screening: a cross-sectional survey of young adults with type 1 and type 2 diabetes in the UK
Bibliographic record
Abstract
Introduction Diabetic retinopathy screening (DRS) attendance in young adults (YAs) is consistently below recommended levels. The aim of this study was to identify barriers and enablers of DRS attendance among YAs in the UK living with type 1 (T1D) and type 2 diabetes (T2D). Research design and methods YAs (18–34 years) were invited to complete an anonymous online survey in June 2021 assessing agreement with 30 belief statements informed by the Theoretical Domains Framework (TDF) of behavior change describing potential barriers/enablers to DRS. Results In total, 102 responses were received. Most had T1D (65.7%) and were regular attenders for DRS (76.5%). The most salient TDF domains for DRS attendance were ‘Goals’, with 93% agreeing that DRS was a high priority, and ‘Knowledge’, with 98% being aware that screening can detect eye problems early. Overall, 67.4% indicated that they would like greater appointment flexibility (Environmental context/resources) and 31.3% reported difficulties getting time off work/study to attend appointments (Environmental context/resources). This was more commonly reported by occasional non-attenders versus regular attenders (59.1% vs 23.4%, p=0.002). Most YAs were worried about diabetic retinopathy (74.3%), anxious when receiving screening results (63%) (Emotion) and would like more support after getting their results (66%) (Social influences). Responses for T1D and T2D were broadly similar, although those with T2D were more likely have developed strategies to help them to remember their appointments (63.6% vs 37.9%, p=0.019) (Behavioral regulation). Conclusions Attendance for DRS in YAs is influenced by complex interacting behavioral factors. Identifying modifiable determinants of behavior will provide a basis for designing tailored interventions to improve DRS in YAs and prevent avoidable vision loss.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".