Prevention Science最新文献

{"title":"The Utility of Machine Learning-Enhanced Developmental Cascade Models in Prevention Science.","authors":"Vanessa Morales, Francisco Cardozo, Raymond R Balise, Sara M St George, Daniel J Feaster","doi":"10.1007/s11121-026-01897-0","DOIUrl":"10.1007/s11121-026-01897-0","url":null,"abstract":"<p><p>Developmental cascade models provide a valuable framework for understanding how risk and protective factors interact over time to shape health and behavioral outcomes. Traditional statistical methods, such as logistic regression and structural equation modeling, have been instrumental in uncovering developmental pathways within prevention science. However, these methods often impose constraints on model complexity and face limitations in capturing the non-linear and interdependent nature of developmental processes. Machine learning (ML) offers complementary advantages, such as the ability to incorporate high-dimensional data, detect complex interactions, and enhance predictive accuracy. These capabilities can improve identification of at-risk individuals, support the timing of interventions across developmental stages, and refine theory-driven models. By integrating ML with developmental cascade models, researchers can more effectively identify when and how which risk accumulates and protective factors exert influence, thereby improving the tailoring and efficiency of prevention strategies. This conceptual paper outlines how ML can extend traditional analytic approaches in developmental cascade research, discusses key practical considerations for researchers including data requirements, software selection, and model validation, and highlights its potential to advance prevention science across the life course.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1153-1162"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13550091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147595687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Who Is Getting the Help They Need? An AI-Driven Study of Intersectional Disparities in Mental Health Service Utilization Among Young Adults with Suicidal Ideation.","authors":"Hayoung K Donnelly, Seonyeong Kim, Suna Kim, Eric S Crosby, Maria A Oquendo, Gregory K Brown, Danielle L Mowery","doi":"10.1007/s11121-026-01924-0","DOIUrl":"10.1007/s11121-026-01924-0","url":null,"abstract":"<p><p>Despite the critical role of mental health services in suicide prevention, disparities in service utilization persist across various individual and social determinants of health. This study identifies key factors and intersectional patterns associated with mental healthcare use among young adults with past-year suicidal ideation, by developing machine learning-based artificial intelligence to improve predictive accuracy while ensuring interpretability for clinicians and policymakers. Utilizing cross-sectional data from the 2015-2020 National Survey on Drug Use and Health, we analyzed 11,018 US young adults aged 18-34 who reported past-year suicidal ideation. Random Forest and Shapley Additive Explanations identified the strongest predictors of mental healthcare utilization among 23 individual and social determinants of health. The decision tree model visualized prediction of utilization rates across intersectional characteristics. Findings indicate that depression, race/ethnicity, sexual orientation, and private health insurance were the strongest predictors of service use. Individuals without depression, males, Black, Indigenous, and People of Color, heterosexual individuals, and individuals without private health insurance were significantly less likely to seek care. These findings highlight the current intersectional disparities in mental healthcare utilization among young adults at risk of suicide. Expanding culturally competent care and promoting equitable access to mental healthcare for young adults at risk are crucial steps in addressing these disparities.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1125-1139"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13550064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147876065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Machine Learning to Uncover Ethnic-Specific Predictors of Maternal Postpartum Depression.","authors":"Ying Zhang, Jun Fang, Andrew Liu","doi":"10.1007/s11121-026-01880-9","DOIUrl":"10.1007/s11121-026-01880-9","url":null,"abstract":"<p><p>Maternal postpartum depression (PPD) is a significant mental health issue that affects over 15% of mothers, with adverse effects on their children. Ethnic disparities in PPD represent a pressing public health concern, and minority mothers are found to have a higher likelihood of being affected by PPD. Identifying specific risk factors for these mothers is crucial for developing targeted preventive strategies that can help mitigate the disparities in PPD. This study used CDC data (N = 39,637) from the Pregnancy Risk Assessment Monitoring System to examine how risk factors for PPD vary among mothers from different ethnic groups. We applied Random Forest machine learning (ML) algorithms, and the final model achieved a mean ROC AUC of 0.65 and PR AUC of 0.66 during cross-validation on balanced samples, though performance declined on the held-out test set (PR AUC = 0.28; two times higher than the baseline of 0.11). The top predictors include prepregnancy and prenatal depression, family income, prior PPD-related visits, WIC participation, employment-based insurance, breastfeeding, pregnancy intention, and parental education. Subgroup analyses revealed both shared and unique predictors across racial and ethnic groups. While the results for White mothers closely aligned with overall trends, smoking and pregnancy termination ranked high among Black mothers. For Hispanic/Latina mothers, unintended pregnancy and smoking were more prominent, whereas, for Asian mothers, infant sleep arrangements, prepregnancy health behaviors, and infant sex emerged as key predictors. This study demonstrates the value of using ML to identify data-driven, population-specific predictors of PPD and highlights the need for culturally sensitive, early preventative approaches in perinatal mental health care.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1052-1063"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146203581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Mixed-Methods Study of Policymakers' Adoption of AI to Support Use of Research Evidence: Implications for Artificial Intelligence in Prevention Policy.","authors":"D Max Crowley, Jonathan Wright, Alex Winters, Damon Jones, Jessica Pugel, Patrick O'Neill, Bethany Shaw, Sarah Hamel, Elizabeth Long, Michael Donovan, Taylor Scott","doi":"10.1007/s11121-026-01925-z","DOIUrl":"10.1007/s11121-026-01925-z","url":null,"abstract":"<p><p>Policymakers are increasingly adopting artificial intelligence (AI) tools to support legislative decision-making, yet there is limited empirical understanding of how these technologies are used and the implications for evidence-based policymaking. General-purpose AI tools, such as large language models (LLMs), present both opportunities for improved efficiency and risks related to misinformation and lack of transparency. This study examines state legislators' use of AI in policymaking and introduces the AIRE Protocol (AI for Informed and Responsible Evidence-use), a structured framework for developing specialized AI tools grounded in validated evidence. We demonstrate the application of the AIRE Protocol through the development of the Results First AI Assistant, designed to enhance policymakers' access to the Results First Clearinghouse. A mixed-methods approach was used. Forty-five US state legislators participated in live interviews to assess AI adoption patterns, perceived benefits, and concerns. The AIRE Protocol guided the rapid prototyping and iterative development of the AI assistant, with input from policymakers, national policy organizations, and technical experts, resulting in tailored evidence based recommendations. While policymakers expressed interest in AI tools for improving access to information under time constraints, they also raised concerns regarding transparency, reliability, and appropriate use. Our findings suggest that AI tools tailored to policymakers' needs-developed using frameworks like AIRE-will facilitate the integration of validated evidence into legislative decision-making while addressing ethical and practical concerns associated with generalized AI solutions.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1253-1267"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13550038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Augmented Prevention Science Needs Community-Engaged Prevention Science: a Framework for Greater Accountability.","authors":"Eric Rice, Lindsay E Young","doi":"10.1007/s11121-025-01842-7","DOIUrl":"10.1007/s11121-025-01842-7","url":null,"abstract":"<p><p>With the ability to approximate human decision-making and to parse through large and complex data, artificial intelligence (AI) technologies such as machine learning algorithms stand to play important roles in diagnosing, treating, and preventing society's most pressing social, health, and academic challenges. However, some AI applications for these ends have resulted in biased and discriminatory outcomes, demonstrating poor accountability to the communities these technologies impact and serve. The foundational premise of this paper is that AI-augmented prevention science needs community engagement to mitigate these harms. Drawing on a decade of work conducted at the University of Southern California's Center for AI in Society, we present a model of community-engaged AI-augmented prevention science. What sets our formalization apart from previous frameworks for community-engaged AI research is its prevention science orientation. We highlight potential roles for community and AI at each stage of the prevention science life cycle, from problem conceptualization to intervention implementation, and delineate when community input can compel moments of critical retreat in that life cycle to remain accountable to community needs and values. We then illustrate how we integrated AI and community-engaged methods in our work to correct racial biases in a predictive risk algorithm used to prioritize vulnerable people experiencing homelessness for housing interventions. Our case study highlights two moments of critical retreat-informed by the values, experiences, and contextual knowledge of our community partners-that led to more equitable and inclusive outcomes. We conclude with recommendations for how to advance community-engaged approaches in AI prevention science research.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1216-1226"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13550034/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145139180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Guide to Constructing Indigenous Statistical Spaces for Prevention Science Research.","authors":"Valentín Quiroz de la Sierra","doi":"10.1007/s11121-026-01911-5","DOIUrl":"10.1007/s11121-026-01911-5","url":null,"abstract":"<p><p>Artificial intelligence (AI)-powered computational methods, such as machine learning and natural language processing, are increasingly applied in deaths of despair research among Indigenous populations. However, their application in Indigenous contexts is often constrained by epistemological misalignment, technical limitations, and ethical concerns. Integrating Indigenous Research Methodologies into AI-powered prevention science research is necessary to support Indigenous Data Sovereignty and address deaths of despair. The Indigenous Computational Approach (ICA) provides a structured reflexive protocol for constructing Indigenous Statistical Spaces that operationalize Indigenous Research Methodologies within computational workflows. ICA aligns four interdependent components: Researcher Standpoint, Indigenous Theoretical Frameworks, AI Data Analysis Technique, and Dissemination and Indigenous Governance. This protocol is supported by operational steps and an accompanying ICA Checklist. A previously published case study on the Indigenous Wholistic Factors Project illustrates the ICA in practice in the context of suicide risk modeling. The case study applied a lasso logistic regression model to structure feature selection on an Indigenous subsample of the 2019-2020 California Healthy Kids Survey (n = 2609). Ten of 17 candidate features were retained, and the model demonstrated strong discrimination (AUC = 0.87) and acceptable calibration (Brier score = 0.10). The ICA does not guarantee different empirical findings or superior model accuracy, but rather it restructures how AI models are designed, validated, and deployed for prevention science research. The ICA provides a replicable protocol for AI-powered prevention science research to support Indigenous self-determination and community-defined well-being.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1239-1252"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13189265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147844569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Machine Learning to Predict Features Within Substance Use Disorder Treatment Service Settings That Increase the Likelihood of Positive Treatment Outcomes.","authors":"Treena Becker, Alberto Gonzalez-Martinez","doi":"10.1007/s11121-026-01888-1","DOIUrl":"10.1007/s11121-026-01888-1","url":null,"abstract":"<p><p>Given the conceptual issues involved in defining and measuring recovery and accordingly substance use disorder (SUD) treatment outcomes, the role of each state's treatment system and social factors, the objective is to examine underlying and interrelated patterns within SUD treatment, outcomes, and recovery. Using a recovery-oriented framework, a Machine Learning Random Forest model was developed to analyze publicly funded SUD treatment services across the United States. The aim was to predict the 10 most important features that increase the likelihood of positive treatment outcomes, defined as less substance use (SU) or abstinence. Over 78% of SUD treatment services were provided to individuals either with Medicaid coverage or were uninsured. The most important feature identified was the number of days in treatment, regardless of setting. The second most important feature was the state and whether various treatment services were available. The third and fourth ranked features were the type of treatment at discharge and at admission, respectively. Housing status, SU self-help group participation, and employment were lower ranked. Referral source was the tenth ranked feature. The length of time in SUD treatment is consistent with the clinical perspective of the individual seeking treatment and continuing in care and recovery support. Individuals in Medicaid-funded treatment live in poverty, with peer support and community who have the least resources to support their recovery journey. States that prioritize behavioral health should coordinate to increase the availability of higher-cost, longer-duration treatment services across state lines, to states with low availability.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1077-1088"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147345379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Validation of an Electronic Health Record-Derived Prediction Model for Preventing COVID-19 Hospitalization and Death.","authors":"R Yates Coley, Rachel Hays, Roy E Pardee, Sharon Fuller, Kristine Rogers, Claire L Allen, David E Arterburn, Robert K Frazier, Daniel J Kent, Sophia Mun, Tolani Mwatha, Paul Thottingal, Emily O Westbrook","doi":"10.1007/s11121-025-01844-5","DOIUrl":"10.1007/s11121-025-01844-5","url":null,"abstract":"<p><p>Hospitalization and death following COVID-19 infection continue to pose a major public health concern and place strain on health system resources. Outpatient antiviral medication can reduce the risk of COVID-19 hospitalization and death for those at risk of poor outcomes, but identifying high-risk populations who may benefit most from treatment is challenging. The objective of this study was to develop and validate a prediction model for the composite outcome of hospitalization or death in the 14 days following COVID-19 infection. Our sample included 67,530 COVID-19 infections documented in outpatient care and occurring between April 1, 2020, and November 1, 2022, for 64,529 Kaiser Permanente Washington patients who did not receive outpatient antiviral treatment; 1378 (2.0%) of these infections resulted in hospitalization or death. Our prediction model, estimated using logistic regression with LASSO variable selection and ridge penalization, included 19 risk factors and showed high performance, including an area under the curve of 0.825 (95% confidence interval 0.813-0.836). Among the 10% of infections with the highest risk predictions, the true positive rate was 48% (46-51%) and the positive predictive value was 9.9% (9.2-10.6%). Supplemental analyses confirmed strong model performance across racial and ethnic subgroups and over time. We also present our process for selecting a risk threshold above which to recommend antiviral treatment and discuss considerations for prospective clinical implementation. This project demonstrates that machine learning tools can be used by health systems to deliver timely, targeted secondary prevention to reduce the risk of serious illness or death.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1023-1035"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13550066/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145330445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing Machine Learning for Predicting PrEP Use Status Among Sexual and Gender Minority Young Adults.","authors":"Callisto Boka, Elizabeth A Yonko, Mehrab Beikzadeh, Kimmo Kärkkäinen, Chenglin Hong, Majid Sarrafzadeh, Ian W Holloway","doi":"10.1007/s11121-025-01872-1","DOIUrl":"10.1007/s11121-025-01872-1","url":null,"abstract":"<p><p>Pre-exposure prophylaxis (PrEP) is a highly effective biomedical prevention tool for HIV yet remains underutilized among key populations, particularly among young sexual and gender minorities (SGM). Recognizing the popularity of specific dating and social media apps among SGM young adults, we leveraged user data from these platforms to build a machine learning (ML) model that could inform targeted, data-driven interventions aimed at improving PrEP uptake and adherence. We adapted eWellness, an Android mobile app, to passively collect data from research participants capturing mobile app usage, keystroke patterns and logs, and GPS location data between 2021 and 2024. These data were used to train a ML model to predict self-reported PrEP use. Model accuracy was evaluated through F1 scores across different data types and feature combinations. Study protocols were developed in collaboration with community partners and adhered to strict ethical and privacy standards. A total of 82 SGM young adults participated, with 46 (56%) reporting PrEP use at baseline. Our machine learning model demonstrated good predictive accuracy for predicting PrEP use and non-use, achieving an F1 score of 0.84 (PrEP use) and 0.82 (non-use) outcomes when incorporating data from all mobile apps, including messaging, dating, and social media mobile apps. By contrast, predictions based solely on social media mobile app usage, language associated with sexual behavior and substance use risk, or location monitoring demonstrated worse accuracy (F1 scores of 0.79/0.75, 0.70/0.57, and 0.70/0.52, respectively). Additional feature extraction methods, as well as various combinations of these features, were also tested. However, none achieved predictive accuracy as well as the model incorporating all mobile app usage data combined. This study demonstrates the potential of machine learning to accurately predict PrEP use status among SGM young adults. The findings offer a foundation for developing more personalized PrEP promotion strategies, particularly among SGM young adults who use social media and dating apps. Future research should assess the model's adaptability across diverse SGM subgroups to further inform intervention development. Registry: ClinicalTrials.gov, ID: NCT04710901, November 9, 2020.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1036-1051"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13550048/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145949499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Use of Machine Learning to Predict Offline Adolescent e-Cigarette Use: a Proof-of-Concept.","authors":"Julie V Cristello, Krzysztof Bogusz, Elisa M Trucco","doi":"10.1007/s11121-026-01922-2","DOIUrl":"10.1007/s11121-026-01922-2","url":null,"abstract":"<p><p>Identification of adolescent e-cigarette use could inform prevention and intervention programming and reduce associated consequences. One way to predict those engaging in use is by examining social media profiles and metrics. Most studies examining substance use content on social media employ self-report or human coding that have methodological limitations. Thus, the current study developed a supervised machine learning algorithm to classify participants into e-cigarette use categories based on Instagram metrics. Participants (n = 67, M_age = 18.27; 64.2% female, 82.1% Hispanic/Latino[a/x], 91% White) in the study provided their Instagram data downloaded through the app. Instagram metrics (i.e., number of followers, number following, number of liked comments, number of liked posts, number of posts, and number of messages) were extracted and included as input features in the model. Adolescents reported their e-cigarette use on a self-report measure. A classification tree method was used to classify participants as engaging in e-cigarette use or not. Data was partitioned into a training and test set using stratified sampling. All analyses were performed in Python. Three input features (number of followers, number of liked posts, and number of messages) were selected through hyperparameter-optimized feature selection. The final model accurately detected e-cigarette use 71% of the time. Findings indicate that supervised learning can predict adolescent e-cigarette use with accuracy consistent with other clinical populations. This study establishes that universal aspects of social media may be harbingers for policy makers and tech companies to provide targeted support and messaging.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":"1089-1098"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13203420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}