Translating Workforce Development Policy Interventions for Community Health Workers: Application of a Policy Research Continuum
Bibliographic record
Abstract
CONTEXT: There is a need for knowledge translation to advance health equity in the prevention and control of cardiovascular disease and type 2 diabetes. One recommended strategy is engaging community health workers (CHWs) to have a central role in related interventions. Despite strong evidence of effectiveness for CHWs, there is limited information examining the impact of state CHW policy interventions. This article describes the application of a policy research continuum to enhance knowledge translation of CHW workforce development policy in the United States. METHODS: During 2016-2019, a team of public health researchers and practitioners applied the policy research continuum, a multiphased systematic assessment approach that incorporates legal epidemiology to enhance knowledge translation of CHW workforce development policy interventions in the United States. The continuum consists of 5 discrete, yet interconnected, phases including early evidence assessments, policy surveillance, implementation studies, policy ratings, and impact studies. RESULTS: Application of the first 3 phases of the continuum demonstrated (1) how CHW workforce development policy interventions are linked to strong evidence bases, (2) whether existing state CHW laws are evidence-informed, and (3) how different state approaches were implemented. DISCUSSION: As a knowledge translation tool, the continuum enhances dissemination of timely, useful information to inform decision making and supports the effective implementation and scale-up of science-based policy interventions. When fully implemented, it assists public health practitioners in examining the utility of different policy intervention approaches, the effects of adaptation, and the linkages between policy interventions and more distal public health outcomes.
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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 | 0.046 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".