Development and pilot evaluation of a train-the-trainer chronic kidney disease program for community health workers
Bibliographic record
Abstract
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.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".