Interventions to increase attendance for diabetic retinopathy screening: a systematic review and meta‐analysis
Bibliographic record
Abstract
Purpose Study objectives were to: 1) determine the effectiveness of interventions to improve diabetic retinopathy screening (DRS) attendance; 2) specify intervention content in terms of behaviour change techniques (BCTs); 3) determine whether interventions that included particular BCTs were more effective in increasing attendance. Methods We searched the Cochrane Library, MEDLINE, EMBASE and clinical trials registers to February 2017 for randomised controlled trials (RCTs) that were designed to improve attendance for DRS or were evaluating general quality improvement (QI) strategies for diabetes care and reported the effect of the intervention on DRS attendance. We did not use any date or language restrictions in the searches. We identified and categorised component BCTs using an established BCT Taxonomy (BCTTv1). Results We included 66 RCTs. QI interventions were multifaceted and targeted patients, healthcare professionals (HCPs) or healthcare systems. Overall, DRS attendance increased by 12% (risk difference (RD) 0.12 [95% CI 0.10‐0.14]) compared with usual care, with substantial heterogeneity in effect size. Both DRS‐targeted and general QI interventions were effective, particularly where baseline DRS attendance was low. All frequently identified BCTs were associated with significant improvements in attendance. Higher effect estimates were observed in sub‐group analyses for the BCTs ‘goal setting (outcome)’ (0.26 [0.16‐0.36]) and ‘feedback on outcomes of behaviour’ (RD 0.22 [0.15‐0.29]) in interventions targeting patients, and ‘restructuring the social environment’ (RD 0.19 [0.12‐0.26]) and ‘credible source’ (RD 0.16 [0.08‐0.24]) in interventions targeting HCPs. Conclusions RCT evidence indicates that QI interventions incorporating specific BCT components are associated with meaningful improvements in DRS attendance compared to usual care.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.042 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".