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Record W4415261830 · doi:10.1080/10852352.2025.2572860

Development and pilot evaluation of a train-the-trainer chronic kidney disease program for community health workers

2025· article· en· W4415261830 on OpenAlexaff
Janet Diaz‐Martinez, Sabrina Sales Martínez, Ayodele Tyndall, Laura Kallus, Carlos M.G. Durán, Aydeivis Jean Pierre, Brenda López, Iván Delgado‐Enciso, Gustavo A. Hernández‐Fuentes, Jessica Mancilla, Michelle M. Hospital

Bibliographic record

VenueJournal of Prevention & Intervention in the Community · 2025
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsCommunity Based Research Centre
FundersNational Kidney Foundation
KeywordsKidney diseaseWorkforce developmentWorkforceTrainerEmpowermentType 2 diabetesDiabetes mellitusCurriculumPatient Empowerment

Abstract

fetched live from OpenAlex

Chronic kidney disease (CKD) affects over 37M U.S. adults, driven by type 2 diabetes and hypertension. Latino populations have 35% higher kidney failure prevalence, worse outcomes, and lower engagement in care and prevention. We co-developed and pilot tested a Train the Trainer CKD curriculum for Community Health Workers (CHWs) serving Latinos with or at risk of CKD to assess feasibility, acceptability, and perceived knowledge gains. CHWs were trained to deliver a culturally tailored one-on-one CKD education. Co designed with Caridad Awareness and Education for Kidney Disease advisory board using community-engaged research principles, adult learning methodologies, and insights from diabetes education, the 20 hour, four session program integrated role play, problem solving, and motivational interviewing. Twelve CHWs participated. Post training evaluations showed high feasibility/acceptability and gains in CKD knowledge, confidence, satisfaction, and empowerment to provide CKD education. Future research should evaluate the long-term impact of CHW-led education on CKD outcomes to identify best practices and sustainable workforce development for CHW.

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)0.042
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0420.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.204
GPT teacher head0.530
Teacher spread0.326 · 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; a candidate call from one teacher head, not a consensus.

Study designObservational
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

Citations1
Published2025
Admission routes1
Has abstractyes

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