A Scoping Review on Community-based Diabetes Screening Interventions: Paving the Pathway to Early Care and Prevention of Diabetes
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review mapped evidence on community-based screening interventions for early detection of prediabetes and type 2 diabetes (T2D), and identified barriers and strategies for developing and implementing such interventions in community settings for diverse populations. RECENT FINDINGS: Using the Arskey & O'Malley and Levac frameworks, we conducted a scoping review that identified 33 studies across 13 countries that developed and tested a community-based T2D screening intervention, utilizing risk assessment and Point-of-Care (POC) glucose testing. Screenings occurred in settings such as pharmacies (21%), faith-based centers (6%), and mobile vans (6%), with most studies from the United States (42%), Australia (16%), and Canada (9%). Post-screening, 89% of interventions offered referrals to primary care, while few connected participants to community programming. Barriers and strategies were mapped to the socioecological model to guide future development and implementation of early detection interventions in community settings. This review identified key factors for successful community-based T2D screening interventions, including adequate resources (i.e., funding and personnel), community engagement efforts, and accessible, feasible screening of T2D in community settings. POC testing proved valuable for early detection through immediate glucose results that would prompt potential interventions. However, challenges remain in ensuring long-term sustainability and feasibility of such approaches, as many interventions encountered high attrition rates due to challenges with referral pathways to health care and community programs, structural inequities, and lack of sustainable follow-up processes. Future research should focus on evaluating the cost-effectiveness and sustainable integration of these community-based T2D screening approaches into health systems for broader impact.
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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 | 0.014 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".