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Record W4384942644 · doi:10.1177/2752535x231189932

Small Steps Towards an Inclusive Diabetes Prevention Program: How Small Steps for Big Changes is Improving Program Equity and Inclusion

2023· article· en· W4384942644 on OpenAlexaff
Kaela Cranston, Megan MacPherson, Jenna AP Sim, Mary E. Jung

Bibliographic record

VenueCommunity Health Equity Research & Policy · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsEquity (law)StakeholderInclusion (mineral)Public relationsBusinessHealth equityType 2 diabetesProgram Design LanguageMedicinePolitical scienceNursingPsychologyComputer scienceDiabetes mellitusPublic healthSocial psychology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.035
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0350.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0120.001
Scholarly communication0.0010.001
Open science0.0020.068
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.444
GPT teacher head0.537
Teacher spread0.092 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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