Implementation of eye screening programmes for patients with diabetes: a systematic map of evidence from five countries
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».