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Record W2510839313 · doi:10.1186/s13643-016-0309-2

Barriers and enablers to diabetic retinopathy screening attendance: Protocol for a systematic review

2016· review· en· W2510839313 on OpenAlexafffund
Ella Graham-Rowe, Fabiana Lorencatto, John G Lawrenson, Jennifer Burr, Jeremy Grimshaw, Noah Ivers, Tünde Pető, Catey Bunce, Jill Francis

Bibliographic record

VenueSystematic Reviews · 2016
Typereview
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsWomen's College HospitalUniversity of TorontoOttawa HospitalUniversity of Ottawa
FundersNational Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of OphthalmologyMoorfields Eye Hospital NHS Foundation TrustUniversity of TorontoUniversity of OttawaUniversity of St AndrewsNewcastle UniversityNational Institute for Health and Care ResearchWomen's College HospitalOttawa Hospital Research Institute
KeywordsMedicineAttendanceDiabetic retinopathyGrey literatureThematic analysisPsychological interventionImplementation researchSystematic reviewQualitative researchMEDLINEDiabetes mellitusNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Diabetic retinopathy is a serious complication of diabetes which, if left untreated, can result in blindness. Population screening among people with diabetes has been shown to be clinically effective; however, suboptimal attendance with wide demographic disparities has been reported. To develop quality improvement interventions to maximise attendance, it is important to understand the theoretical determinants (i.e. barriers and enablers) of screening behaviour. The aim of this systematic review is to identify and synthesise the modifiable barriers and enablers associated with diabetic retinopathy screening attendance. METHODS/DESIGN: Primary and secondary studies will be included if they report perceived barriers/enablers of diabetic retinopathy screening attendance, from the perspectives of people with diabetes and healthcare providers. There will be no restrictions on study design. Studies will be identified from published and grey literature through multiple sources. Bibliographic databases will be searched using synonyms in four search domains: diabetic retinopathy; screening; barriers/enablers; and theoretical constructs relating to behaviour. Search engines and established databases of grey literature will be searched to identify additional relevant studies. Extracted data will include: participant quotations from qualitative studies, statistical analyses from questionnaire and survey studies, and interpretive descriptions and summaries of results from reports. All extracted data will be coded into domains from the Theoretical Domains Framework (TDF) and (for organisational level data) the Consolidated Framework of Implementation Research (CFIR); with domains representing theoretical barriers/enablers proposed to mediate behaviour change. The potential role of each domain in influencing retinopathy screening attendance will be investigated through thematic analysis of the TDF/ CFIR coding. Domain importance will be identified using pre-specified criteria: "frequency" and "expressed importance". Variations in perceived barriers and enablers between demographic groups (e.g., socio-economic, ethnic) will be explored. DISCUSSION: This review will identify important barriers and enablers likely to influence attendance for diabetic retinopathy screening. The results will be used to assess the extent to which existing interventions targeting attendance address the theoretical determinants of attendance behaviour. Findings will inform recommendations for future intervention design. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016032990.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.087
metaresearch head score (Gemma)0.098
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.112
Threshold uncertainty score0.458

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0870.098
Meta-epidemiology (narrow)0.0060.007
Meta-epidemiology (broad)0.0160.017
Bibliometrics0.0130.012
Science and technology studies0.0050.005
Scholarly communication0.0080.009
Open science0.0050.006
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.1120.014

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.

Opus teacher head0.082
GPT teacher head0.423
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreProtocol

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".

Quick stats

Citations36
Published2016
Admission routes2
Has abstractyes

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