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BACKGROUND: 1.8 million Veterans are estimated to need legal services, such as for housing eviction prevention, discharge upgrades, and state and federal Veterans benefits. While having ones legal needs met is known to improve ones health and its social determinants, many Veterans legal needs remain unmet. Public Law 116-315 enacted in 2021 authorizes VA to fund legal services for Veterans (LSV) by awarding grants to legal service providers including nonprofit organizations and law schools legal assistance programs. This congressionally mandated LSV initiative will award grants to about 75 competitively selected entities providing legal services. This paper describes the protocol for evaluating the initiative. The evaluation will fulfill congressional reporting requirements, and inform continued implementation and sustainment of LSV over time. METHODS: Our protocol calls for a prospective, mixed-methods observational study with a repeated measures design, aligning to the Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) and Integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) frameworks. In 2023, competitively selected legal services-providing organizations will be awarded grants to implement LSV. The primary outcome will be the number of Veterans served by LSV in the 12 months after the awarding of the grant. The evaluation has three Aims. Aim 1 will focus on measuring primary and secondary LSV implementation outcomes aligned to RE-AIM. Aim 2 will apply the mixed quantitative-qualitative Matrixed Multiple Case Study method to identify patterns in implementation barriers, enablers, and other i-PARIHS-aligned factors that relate to observed outcomes. Aim 3 involves a mixed-methods economic evaluation to understand the costs and benefits of LSV implementation. DISCUSSION: The LSV initiative is a new program that VA is implementing to help Veterans who need legal assistance. To optimize ongoing and future implementation of this program, it is important to rigorously evaluate LSVs outcomes, barriers and enablers, and costs and benefits. We have outlined the protocol for such an evaluation, which will lead to recommending strategies and resource allocation for VAs LSV implementation.

ObjectivesSurveys can assist in screening oral diseases in populations to enhance the early detection of disease and intervention strategies for children in need. This paper aims to develop short forms of child-report and proxy-report survey screening instruments for active dental caries and urgent treatment needs in school-age children.MethodsThis cross-sectional study recruited 497 distinct dyads of children aged 8-17 and their parents between 2015 to 2019 from 14 dental clinics and private practices in Los Angeles County. We evaluated responses to 88 child-reported and 64 proxy-reported oral health questions to select and calibrate short forms using Item Response Theory. Seven classical Machine Learning algorithms were employed to predict children's active caries and urgent treatment needs using the short forms together with family demographic variables. The candidate algorithms include CatBoost, Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes, Neural Network, Random Forest, and Support Vector Machine. Predictive performance was assessed using repeated 5-fold nested cross-validations.ResultsWe developed and calibrated four ten-item short forms. Naïve Bayes outperformed other algorithms with the highest median of cross-validated area under the ROC curve. The means of best testing sensitivities and specificities using both child-reported and proxy-reported responses were 0.84 and 0.30 for active caries, and 0.81 and 0.31 for urgent treatment needs respectively. Models incorporating both response types showed a slightly higher predictive accuracy than those relying on either child-reported or proxy-reported responses.ConclusionsThe combination of Item Response Theory and Machine Learning algorithms yielded potentially useful screening instruments for both active caries and urgent treatment needs of children. The survey screening approach is relatively cost-effective and convenient when dealing with oral health assessment in large populations. Future studies are needed to further leverage the customize and refine the instruments based on the estimated item characteristics for specific subgroups of the populations to enhance predictive accuracy.