Digital Health Tool for Preventing Blindness From Diabetic Retinopathy: Protocol for a Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Diabetic retinopathy (DR), a leading cause of preventable blindness among working-age adults, leads to worse health outcomes among Black, Latine, and individuals with lower income in comparison to other ethnic, racial, and socioeconomic groups in the United States. OBJECTIVE: We aim to engage community members directly to identify barriers and facilitators of DR screening and co-design a digital health tool that is accessible, user-friendly, and community-responsive. METHODS: We conducted focus groups with individuals from the Greater New Haven, Connecticut area, aged 18 years or older, and diagnosed with diabetes to (1) conduct a comprehensive disease-management needs assessment and (2) inform the development of a community-responsive digital health tool to optimize DR education and to increase access to DR screening in high-risk populations. We transcribed the focus group interviews, used rapid qualitative analysis to generate themes, and completed affinity mapping to identify content and features for a digital health tool for preventing blindness from DR. RESULTS: of 6.77 (SD 1.93). Approximately 58% (11/19) of the participants completed some college. Frequently occurring themes (>30) obtained from coding contextual interviews were desired app attributes such as options for customization, covering topics such as, the mental impact of living with diabetes, informal support (eg, peer support), diabetes education, barriers to controlling blood sugar and maintenance of health, dietary guidance, and notifications. We created high-fidelity wireframes incorporating key features. Next, we will iteratively design a prototype with community feedback. CONCLUSIONS: We anticipate that this study will characterize the experiences of people living with diabetes. Using a community-led approach,we will continue to analyze the data that will generate insights regarding the key features, content, and benefits that are most important for the creation of a digital health tool for DR prevention. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/65894.
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 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 | 0.062 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.072 | 0.011 |
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".