Show Me CKDintercept Initiative: A Collective Impact Approach to Improve Population Health in Missouri
Bibliographic record
Abstract
Ninety percent of people with chronic kidney disease (CKD) remain undiagnosed, most people at risk do not receive guideline-concordant testing, and disparities of care and outcomes exist across all stages of the disease. To improve CKD diagnosis and management across primary care, the National Kidney Foundation launched a collective impact (CI) initiative known as Show Me CKDintercept. The initiative was implemented in Missouri, USA from January 2021 to June 2022, using a data strategy, stakeholder engagement and relationship mapping, learning in action working groups (LAWG), and a virtual leadership summit. The Reach, Effectiveness, Adoption, Implementation, and Maintenance framework was used to evaluate success. The initiative united 159 stakeholders from 81 organizations (Reach) to create an urgency for change and engage new CKD champions (Effectiveness). The adoption resulted in 53% of participants committed to advancing the roadmap (Adoption). Short-term results reported success in laying a foundation for CI across Missouri. The long-term success of the CI initiative in addressing the public health burden of kidney disease remains to be determined. The project reported the potential use of a CI initiative to build leadership consensus to drive measurable public health improvements nationwide.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".