Community-based type 2 diabetes screening programmes designed for priority populations: a scoping review protocol
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes is a growing public health concern, and it continues to disproportionately impact priority populations. Although earlier and more frequent screening of diabetes promotes early detection to prevent adverse outcomes, this is a significant barrier for priority populations due to inequities that hinder access to critical preventive screening in primary care settings. The purpose of this scoping review is to better understand the design and implementation of screening and early detection of type 2 diabetes in community settings for priority populations to reduce missed or delayed diagnoses and future potential adverse outcomes. METHODS: recommendations. A search strategy was designed using insights from experienced librarians through the Peer Review for Electronic Search Strategies to conduct a comprehensive search using the following databases: Medline, Embase, PsycINFO, Web of Science, Scopus, CINAHL and Google. The search will capture studies focused on community-based diabetes screening using point-of-care testing and deployed in community settings serving priority populations with undiagnosed diabetes. Studies will be excluded if priority populations were not a focus, individuals living with diabetes, the intervention is not implemented in a community setting and did not use point-of-care screening. Two authors will independently review and screen the articles (title, abstract and full-text), while a team-based approach will be applied to chart the data. A thematic analysis will be used to identify emerging themes and subthemes according to barriers and enablers of implementing an equitable community-based diabetes screening intervention. ETHICS AND DISSEMINATION: The findings from this review will inform future diabetes screening interventions in community settings to enable an equity-informed approach in the design, planning and implementation of such strategies. Equally important, it will inform a larger project, in which the team plans to implement a community-based diabetes screening programme in Ontario, Canada.
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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 | 0.120 | 0.083 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.079 | 0.017 |
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".