Small Steps Towards an Inclusive Diabetes Prevention Program: How Small Steps for Big Changes is Improving Program Equity and Inclusion
Bibliographic record
Abstract
Social determinants of health, the effects of colonialism, and systemic injustices result in some groups being at disproportionately higher risk for developing type 2 diabetes (T2D). Many T2D prevention programs have not been designed to provide equitable and inclusive care to everyone. This paper presents an example of the steps taken in an evidence-based community T2D prevention program, Small Steps for Big Changes (SSBC), to improve equitable access and inclusivity based on input from a stakeholder advisory group and the ConNECT Framework. To improve reach to those most at risk for T2D, SSBC has changed both eligibility criteria and program delivery. To ensure that all testing is done in an inclusive manner, changes have been made to measurements, and to training for those delivering the program. This paper also provides actionable recommendations for other researchers to incorporate into their own health programs to promote inclusivity and ensure that they reach those most at risk of T2D.
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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 | 0.054 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".