The continuous process of making research inclusive: Examples offered from the small steps for big changes diabetes prevention program
Bibliographic record
Abstract
Small Steps for Big Changes is an evidence-based diabetes prevention program which is being implemented in the community in partnership with the YMCA in British Columbia, Canada. It was designed to be a sustainable and accessible community program. While Small Steps for Big Changes has been shown to be effective in helping individuals with prediabetes to reduce their risk of developing T2D, there is room for improvement in making the program more inclusive to everyone, especially those at increased risk for T2D due to systemic factors. The purpose of this presentation is to describe the steps taken within Small Steps for Big Changes (SSBC) to improve equitable access and inclusivity within the program. The changes made within SSBC can be used as examples for making other community health programs more inclusive. Changes include, but are not limited to: budget-friendly food and exercise recommendations, involving stakeholders in decision-making, offering a virtual program option, training coaches in cultural safety and inclusivity, increasing the diversity of people shown in promotional materials, expanding inclusion criteria, and making measurements more inclusive and safe. In addition to outlining and justifying changes made to Small Steps for Big Changes, this presentation also provides actionable recommendations for other researchers to incorporate into their own health programs to promote inclusivity and ensure that they reach those most affected by health inequities.
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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 | 0.056 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".