Prevention Science最新文献

{"title":"Designing Rosie the Chatbot with and for Pregnant and New Mothers of Color:  a Community-Engaged Study Leveraging Artificial Intelligence and Prevention Science to Improve Maternal and Child Health Outcomes.","authors":"Elizabeth M Norell, Amara Channell Doig, Michelle Jasczynski, Alexis S Hunter, Francia Ximena Marin Gutierrez, Heran Mane, Sourabh Mane, Xiaohe Yue, Quynh C Nguyen","doi":"10.1007/s11121-026-01901-7","DOIUrl":"https://doi.org/10.1007/s11121-026-01901-7","url":null,"abstract":"<p><p>Maternal and child health is widely recognized as a marker for a healthy society. According to the Centers for Disease Control and Prevention, the US maternal mortality rates have remained high, with over 1200 maternal deaths occurring in 2021. Recognizing that systemic racism is embedded at all levels of the public health and medical fields, intervention is needed at all levels of the socio-ecological model. Developing health communication tools for pregnant women and new parents is one leverage point in the numerous changes that must occur across public health and medical fields to achieve maternal and child equity. The current study employed focus groups to inform development of an interactive question-and-answer chatbot called Rosie. Participants (n = 30) were all pregnant and new mothers of color residing in the United States. Data were collected in virtual focus groups (N = 6) and transcribed verbatim. Template analysis of focus group transcripts produced three themes in women's health information needs and preferences: (1) Pregnancy and New Parenthood Challenges, (2) Sources of Information and Support, and (3) Chatbot Design. Chatbots as a purveyor of health education information were perceived as a promising approach among our pregnant and new mothers of color participants, who had an array of needs that could be addressed by an intervention such as a chatbot. This technology has broad applicability in the health sphere and may serve as an important supplement to both clinical care and existing early childhood intervention services.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147718466","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":"The R-CITY Youth Violence Preventive Intervention: Primary Outcomes from a School Cluster Randomized Controlled Trial.","authors":"Jessika H Bottiani, Meredith P Franco, Michelle K Francis, Kate Somerville, Chelsea A Kaihoi, Elise T Pas, Catherine P Bradshaw","doi":"10.1007/s11121-026-01907-1","DOIUrl":"https://doi.org/10.1007/s11121-026-01907-1","url":null,"abstract":"<p><p>Student experiences of racism and discrimination in schools can undermine their sense of safety and psychological wellbeing and contribute to aggression and violence. Yet educational systems rarely implement violence prevention programming with bias prevention or equity promotion components. To address this gap, researchers and educators partnered to develop R-CITY (Reducing Racism and Violence through Collaborative Intervention with Teachers and Youth; Bottiani et al., School Mental Health, 16(3), 632-648, 2024). A school-level randomized controlled trial was conducted in 27 elementary and middle schools to assess the 'value-added' benefits of supplementing the standard Second Step SEL program (22-27 lessons and group implementation support; comparison condition) with R-CITY's equity-focused one-to-one teacher coaching and grade-differentiated sets of six equity lessons with implementation supports (Second Step + R-CITY, intervention condition). Augmenting Second Step with R-CITY equity-focused components was associated with significant effects on one of six observational measures of student behavior (physical aggression) and one of three teacher-report measures (general teaching self-efficacy), both in the hypothesized direction. Sensitivity analyses excluding the most severely COVID-impacted cohort identified an additional effect on teacher-reported racial discomfort. No significant effects were found on observed teacher practice outcomes or suspension disproportionality rates. Results provide initial evidence that supplementing traditional SEL programming with equity content and coaching can produce significant incremental effects on select outcomes, including reductions in physical aggression and improvements in teacher capacity; however, further research is needed to evaluate the intervention's cost-effectiveness and effects on equity-specific outcomes.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147692277","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":"The Impact of Nurse Home-Visiting for Pregnant and Parenting Individuals with Previous Live Births.","authors":"Gregory J Tung, Venice N Williams, Michael D Knudtson, Carol Franco-Rowe, Bridget Mosley, Chris Aristides, David L Olds, Mandy A Allison","doi":"10.1007/s11121-026-01890-7","DOIUrl":"https://doi.org/10.1007/s11121-026-01890-7","url":null,"abstract":"<p><p>The Nurse-Family Partnership (NFP) benefits first-time parents and their children; however, its effectiveness for families with previous children (multiparous) is not known. This quasi-experimental study used electronic health record data from three NFP sites affiliated with large health systems to evaluate the impact of NFP on pregnancy, birth, and health care utilization outcomes among multiparous families compared with propensity score-matched Medicaid-insured families who did not receive NFP. The study population included 639 multiparous pregnant individuals enrolled in NFP between 2017 and 2021 and a matched comparison group of 6243 non-NFP Medicaid-insured individuals. No statistical differences were found between groups in preterm birth (OR 1.02, 95% CI [0.99, 1.05], p = 0.166), birth weight > 2500 g (OR 1.02, 95% CI [1.00, 1.05], p = 0.094), gestational hypertension (OR 1.02, 95% CI [1.00, 1.05], p = 0.084), child emergency department visits (OR 1.03, 95% CI [0.97, 1.09], p = 0.329), or hospitalizations for injuries (OR 0.0997, 95% CI [0.99, 1.00], p = 0.0503). NICU length of stay was lower for NFP participants (mean difference - 2.45 days) but did not reach statistical significance (95% CI [- 4.91, 0.02], p = 0.052). NFP participants also had higher odds of receiving long-acting reversible contraception (LARC) (OR 1.04, 95% CI [1.01, 1.08], p = 0.019) though this was not significant after adjusting for multiple comparisons. NFP participants did have significantly higher odds of receiving a postpartum visit within 6 weeks (OR 1.22, 95% CI [1.16, 1.24], p < 0.001) and recommended well-child visits (percentage point increase 6.61, 95% CI [3.36, 9.59], p = 0.001). NFP participation among multiparous families was associated with some health care utilization outcomes. These findings suggest a potential mechanism by which NFP may contribute to long-term maternal and child health improvements and highlight the need for further research to assess its effectiveness in this population.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147692949","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":"Sexual Health and Relationship Education Needs of People in Recovery for an Opioid Use Disorder.","authors":"J Michael Wilkerson, Kathryn R Gallardo, I Niles Zoschke, Hannah L N Stewart, Serena A Rodriguez, Michael U Anosike, Jason Pullin, Sheryl A McCurdy","doi":"10.1007/s11121-026-01909-z","DOIUrl":"https://doi.org/10.1007/s11121-026-01909-z","url":null,"abstract":"<p><p>People in recovery with histories of sex and drug-linked behavior (SDB) have an increased risk of substance use recurrence. However, sexual health concerns remain largely unaddressed by recovery support services researchers and practitioners. The purpose of this analysis was to describe the sex and relationship concerns of people in recovery for an opioid use disorder living in level II and level III recovery homes. Recovery homes are sober living homes that the National Association for Recovery Residences classifies into four levels based on staffing and service provision. We interviewed 93 residents and thematically analyzed 92 of the resulting transcripts; one was excluded because the participant did not talk about SDB. Most participants avoided sexual experiences or romantic relationships while living in recovery homes. Memories of SDB can trigger unwanted substance use recurrence, and histories of sexual trauma or reliance on drugs during sex impede connection with potential sex or romantic partners. Participants wanted to heal and prepare for healthy sexual or romantic relationships. Recovery residents could benefit from sexual health education that provides the skills for healthy sexual or romantic relationships.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677798","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":"Discrimination, Racism, and Structural Determinants in Youth Violence Prevention Programs: A Scoping Review and Intervention Component Analysis.","authors":"Mackenzie Weise, Leslie Marie Manso, Shikha Chandarana, Mark Edberg","doi":"10.1007/s11121-026-01906-2","DOIUrl":"https://doi.org/10.1007/s11121-026-01906-2","url":null,"abstract":"<p><p>This scoping review maps youth violence prevention studies (1990-2025) that explicitly incorporate discrimination, racism, or structural terms in titles/abstracts, categorizes violence outcomes, and analyzes interventions with components specifically designed to address these factors. From 1034 unique records across a multi-disciplinary group of eight databases, a very small fraction of eligible studies that explicitly centered and named discrimination-related concepts was identified (n = 14). Among these, the use of \"discrimination\" and \"structural\" terminology was most salient. Violence outcomes were inconsistently operationalized but most included subjective measures of youth problem behavior or aggression; objective outcome measures related to formalized offenses were moderately used. Five studies featured programs targeting discrimination, racism, or structural determinants and qualified for intervention component analysis (ICA). Most of the studies leveraged multicomponent interventions, but reporting on change mechanisms varied widely. This review highlights a gap between rhetoric around the role of discrimination and structural factors in youth violence and how interventions are designed, indexed, and evaluated. Our ICA findings suggest practical models for embedding discrimination or structural components in prevention programs. Rather than identifying all programs impacting these factors, this review maps those explicitly focused on them. Findings urge funders, journals, and leaders to prioritize evaluations which clearly articulate how prevention interventions address upstream drivers of youth violence.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677828","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":"State-Level Extreme-Risk Protection Order Policies, Mental Health, and Gun Carrying: Demographic and Racial Disparities in U.S. Youth.","authors":"Xiang Gao","doi":"10.1007/s11121-026-01912-4","DOIUrl":"https://doi.org/10.1007/s11121-026-01912-4","url":null,"abstract":"<p><p>Gun carrying among U.S. high school students increases risks for violence and injury. Poor mental health is linked to carrying guns, but less is known about how this link varies across demographic and racialized groups. Extreme-risk protection orders (ERPOs) allow temporary removal of firearms from individuals at high risk of harming themselves or others, including during a mental health crisis. Their potential to reduce disparities in youth gun carrying is not well studied. This study assessed whether ERPOs modify the association between poor mental health and gun carrying in high school students overall and subgroups. Data came from the 2023 pooled state Youth Risk Behavior Surveillance System. Gun carrying was the outcome, poor mental health was the predictor, and ERPOs were the moderator, categorized as EROP (less restrictive), Ex parte (moderately restrictive), and Ex parte expanded (more restrictive). Sex, grade, and race were included. Multilevel mixed-effects logistic regression models tested interactions between ERPOs and poor mental health. Demographic and racialized differences in gun carrying were identified. Males had higher odds of carrying guns than females. Poor mental health was associated with higher odds of gun carrying overall and in subgroups, with stronger associations among males, 12th graders, African American students, and Hispanic students. Across the sample and subgroups, ERPOs were linked to reduced gun carrying among students reporting poor mental health. Findings suggest that ERPOs may lower the risk of gun carrying linked to poor mental health and may also reduce demographic and racialized disparities in youth firearm harm.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147663344","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":"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":"https://doi.org/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":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neighborhood Opportunity and Genetic Literacy in a Representative Sample of US Adults.","authors":"Jemar R Bather, Melody S Goodman, Kimberly A Kaphingst","doi":"10.1007/s11121-026-01908-0","DOIUrl":"https://doi.org/10.1007/s11121-026-01908-0","url":null,"abstract":"<p><p>Research shows that genetic literacy varies as a function of individual-level factors, but these factors may not account for all observed differences in genetic literacy. We tested the hypothesis that neighborhood opportunity-a structural factor-is associated with genetic literacy. We analyzed nationally representative cross-sectional data on a weighted sample of 606 US adults from the 2024 Measurement of Genetic Literacy Survey. The Genetic Literacy and Comprehension measure assessed genetic literacy ( <math><mi>α</mi></math> = 0.87). The Childhood Opportunity Index 3.0 measured overall neighborhood opportunity and three domains (Education, Health and Environment, Social and Economic resources). Unadjusted and adjusted weighted linear regression models quantified the associations between neighborhood opportunity and genetic literacy. Among the weighted sample (mean age = 48, SD = 18), 52% were female, and 61% were as non-Hispanic White. Very low overall neighborhood opportunity was significantly associated with lower genetic literacy (β =  - 0.70, 95% CI: - 1.40 to - 0.04, p = 0.037), adjusting for demographic characteristics, health-related factors, and receipt of genetic testing. We observed a similar pattern for exposure to very low social and economic resources (β =  - 0.95, 95% CI: - 1.60 to - 0.31, p = 0.004). There was no evidence of a statistically significant association between the Health and Environment domain and genetic literacy in the final model (β =  - 0.13, 95% CI: - 0.64 to - 0.38, p = 0.62). Findings indicate that neighborhood opportunity is associated with genetic literacy. These results reinforce the importance of assessing structural factors along with individual-level characteristics in genetic literacy research.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147575974","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":"Comparing AI-Assisted Coding and Traditional Qualitative Analysis: a Study Examining Differences in Methods and Results of AI-Assisted Coding and Traditional Content Coding Using Community Engagement Data Collected During the Development of a Municipal Food Plan.","authors":"Natalie Poulos, Hannah Price, Lauren Bell, Courtney Byrd-Williams, Sergio Torres-Peralta, Edwin Marty","doi":"10.1007/s11121-026-01904-4","DOIUrl":"https://doi.org/10.1007/s11121-026-01904-4","url":null,"abstract":"<p><p>Qualitative data poses a challenge for prevention science and public health, as it is critical to explain the context of communities, health, and behavior, yet collecting and analyzing qualitative data using traditional methods is time-intensive and requires extensive training. As artificial intelligence (AI) models have improved, there is a growing interest in using AI to code qualitative data quickly and reliably. This study compares the similarities and differences in methods and results of artificial intelligence (AI)-assisted qualitative analysis to traditional qualitative content analysis using data collected during the development of a city and county-based food plan. In total, 2820 community comments were collected across 43 community events in 27 zip codes across the region between March 2023 and January 2024. AI-assisted analysis was completed using a combination of a transcription app (Post-It^Ⓡ), GPT4 Plus, and GPT for Sheets with oversight from a public health practitioner. Traditional qualitative content analysis was completed with two trained coders who completed codebook development, reliability analysis, and full content coding. Both methods used deductive codes to represent key aspects of the food system and generated inductive codes to represent areas not included by the deductive food system codes. Results found that AI-assisted methods and traditional content analysis produced similar deductive coding results, while inductive coding results were less comparable across methods. Given that qualitative data has become a central part of prevention science, we believe with careful considerations, AI-assisted methods with intentional oversight have the potential to strengthen our ability to process large amounts of qualitative data.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147522388","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":"Building and Validating an Explainable Machine Learning Model for Predicting Health-Promoting Behaviors in Older Adults: A Multicenter Study.","authors":"Pingping Zhang, Yutong Hou, Yidan Zhai, Ye Tian, Yang Yang, Tingting Li, Dezhi Lu, Liang Zhou, Tao Wu","doi":"10.1007/s11121-026-01898-z","DOIUrl":"https://doi.org/10.1007/s11121-026-01898-z","url":null,"abstract":"<p><p>Enhancing health-promoting behaviors (HPBs) in older adults is crucial for chronic disease management and healthy aging in the context of population aging. Accurate assessment of individual HPB levels can facilitate the development of personalized interventions. This study aimed to identify factors influencing HPBs in older adults using multicenter data and to develop and validate an interpretable machine learning (ML) model for prediction. We conducted a multicenter cross-sectional study among 781 older adults in Shanghai, Jiangsu, and Shandong from June 2024 to September 2025. The collected data included sociodemographic characteristics, health status, community sports facility conditions, mobile phone proficiency, and internet skills. Data from the Shanghai (n = 319) and Shandong (n = 228) centers formed the training set, and data from the Jiangsu center (n = 234) constituted the independent external test set. Model discrimination was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, specificity, positive and negative predictive value (PPV, NPV), recall, and F1-score. Calibration was assessed with the Hosmer-Lemeshow test and Brier score, and clinical utility was evaluated via decision curve analysis (DCA). The mean age of participants was 61.79 ± 11.54 years. Based on HPB levels, 436 (55.8%) participants were categorized into the HPB group and 345 (44.2%) into the no HPB group. On the external test set, the Stochastic Gradient Boosting Trees (SGBT) model demonstrated optimal performance, with an area under the curve (AUC) of 0.891 (95% CI, 0.848-0.951), excellent calibration (Brier score = 0.103), and a calibration curve closely aligned with the ideal line. Additional metrics included accuracy (0.895), specificity (0.867), PPV (0.897), NPV (0.892), recall (0.917), and F1-score (0.907). DCA indicated a high net clinical benefit across a wide probability threshold range (0-0.6). SHAP analysis elucidated the contribution of each feature, and a user-friendly online prediction platform was deployed. We developed a high-performance, interpretable ML model to predict HPBs in older adults, and systematically identified key predictors such as internet proficiency, educational level, and functional independence. This tool can assist healthcare professionals in rapidly assessing HPB levels, facilitating the precise delivery of health information and services.</p>","PeriodicalId":48268,"journal":{"name":"Prevention Science","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147515668","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}