Scaling an Evidence‐Based Community Health Worker Program With Fidelity: Results and Lessons Learned
Bibliographic record
Abstract
Policy Points Effectively implemented community health worker (CHW) programs improve patient health outcomes and quality of care, reduce health care costs, and are a key strategy for addressing social and structural drivers of health. As policymakers consider funding mechanisms for CHW programs, it is crucial to tie funding to evidence-based best practices while also allowing for innovation and context-specific adaptations. CONTEXT: Community health worker (CHW) programs represent a key strategy for addressing social and structural drivers of health and have the potential to improve patient health outcomes and enhance quality of care while reducing health care costs. However, challenges such as high staff turnover, lack of program infrastructure, and inadequate CHW support and supervision can hinder implementation and sustainment of effective CHW programs. Furthermore, few CHW programs have been successfully scaled across multiple organizations and communities. Individualized Management for Person-Centered Targets (IMPaCT) is an evidence-based CHW model designed to address these challenges by standardizing processes for CHW hiring, training, support, and supervision while still allowing for context-specific adaptation and tailoring. In this dissemination and implementation project, we evaluated implementation of IMPaCT across five geographically and structurally distinct sites serving diverse and varied patient populations. METHODS: Model fidelity was assessed across seven best practice domains via structured virtual observations with CHWs, supervisors, and program directors at each implementation site. Acute care use was evaluated using difference-in-differences regression modeling for patients enrolled in IMPaCT compared with a propensity score-matched control group. All implementation sites examined total hospital days per patient, and several sites chose to incorporate additional measures of acute care use such as the number of hospitalizations and emergency department visits. FINDINGS: We found that core program components were implemented consistently across sites, and three of five sites were able to both sustain implementation over a three-year period and demonstrate significant reductions in acute care use, consistent with previous randomized controlled trials of this program. CONCLUSIONS: Health systems may be able to address social drivers of health and improve population health for patients who are low-income and patients of color by implementing evidence-based CHW programs with fidelity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".