Journal of Behavioral Medicine最新文献

{"title":"Community voices, collective solutions: Integrating secondary traumatic stress prevention into medical interpreter training using community-based participatory research.","authors":"Lucybel Mendez, Cynthia M Navarro Flores, Dalia Rodriguez-Rojo, Alejandro L Vázquez, Brandon Ledford, Oswaldo Moreno","doi":"10.1007/s10865-026-00662-6","DOIUrl":"10.1007/s10865-026-00662-6","url":null,"abstract":"<p><p>Medical interpreters are frequently exposed to emotionally charged and potentially traumatic patient narratives in their work, increasing their risk for developing secondary traumatic stress (STS) and contributing to burnout, compassion fatigue, and turnover. These occupational stressors represent trauma embedded within healthcare delivery systems, rather than solely an individual-level burden, with downstream effects on systems of care that affect patients (e.g., communication, adherence, disclosure, trust), workforce retention, healthcare quality, and health equity. Despite these detrimental outcomes, there are currently no universal STS prevention programs or institutional guidelines, which limits the dissemination of STS training among medical interpreters. The present practice-oriented study describes a community-based participatory research (CBPR) partnership between university researchers and a community organization housing a Spanish–English interpreter training program serving predominantly Latine patients. We discuss the partnership building process using a CBPR logic model describing the most salient factors influencing the initiative relating to the context, group dynamics, systemic intervention, and anecdotal outcomes. We discuss a three-component, trauma-informed systemic intervention for preventing STS in medical interpreters that emerged from this collaboration, which includes psychoeducation and skills training, routine monitoring, and structured processing groups. We describe anecdotal lessons learned from this collaboration relating to the use of a CBPR approach, the importance of reflexivity and flexibility, ways of further developing the systemic intervention, and the tension between CBPR and neoliberalism in academia. Finally, implications, limitations, and future directions are provided, including the need for quantitative/qualitative evaluations of the described integrated STS prevention framework for medical interpreter training programs.</p>","PeriodicalId":48329,"journal":{"name":"Journal of Behavioral Medicine","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147785903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intertwined adversities: a mixed methods study of ACEs and migration trauma among Latinx immigrants.","authors":"Monica Estrada, Toriah J Haanstad, Norma Reyes, Carisa M Marchena, Sabreet Kaur Dhatt, Joshua Estrada-Serrano, Gabriela A Nagy","doi":"10.1007/s10865-026-00664-4","DOIUrl":"10.1007/s10865-026-00664-4","url":null,"abstract":"<p><p>Latinx immigrants experience significant mental health inequities shaped by adverse childhood experiences (ACEs) and migration-related adversity. Although these facets of adversity have been examined extensively, research often operates in silos, limiting a comprehensive understanding of their interconnected nature. This exploratory mixed methods study examines associations among ACEs, migration-related adversity, and perceived social support among adult Latinx immigrants residing in an ethnic enclave of a hyper-segregated Midwestern city. Using quantitative surveys from a pilot of the Cultivating Resilience intervention (N = 50) and semi-structured interviews with a subsample (n = 16), we performed rapid qualitative and exploratory quantitative analyses separately and integrated findings to derive overarching insights. Integrated analyses yielded six overarching themes: (1) Salient types of adversity; (2) Associations between distinct types of adversity in early childhood; (3) Unmet emotional needs and social support outcomes; (4) The fulfillment of social support; and (5) The perpetuation of intergenerational trauma. Advancing integrated knowledge of adversity among Latinx immigrants is a critical public health goal, as our findings suggest the chronic and compounding nature of these experiences and the central role of perceived social support, highlighting the need for culturally and trauma-informed interventions that strengthen and coordinate support systems within evolving familial dynamics.</p>","PeriodicalId":48329,"journal":{"name":"Journal of Behavioral Medicine","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147718448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Representing subpopulations with latent profile analysis: a non-technical introduction using exercisers' goal orientation adoption profiles.","authors":"E Whitney G Moore, Alessandro Quartiroli","doi":"10.1007/s10865-025-00596-5","DOIUrl":"10.1007/s10865-025-00596-5","url":null,"abstract":"<p><p>Latent profile analysis (LPA) is in the finite mixture model analysis family and identifies subgroups by participants' responses to continuous variables (i.e., indicators); participants' probable membership in each subgroup is based on the similarity between the subgroup's prototypical responses and the person's unique responses. Compared to latent class analysis (LCA) with categorical data, LPA is a better fit for many variables and theories in behavioral medicine, because LPA can have continuous item, sub-scale, or scale scores as indicators, which can enable identifying and examining subgroups defined by responses representing complex, multidimensional concepts (e.g., orientations, motivations, well-being, ill-being, physical activity engagement) and biomarkers of diseases and disorders. Recently, the use of LPA has increased and as it continues to evolve, it is important researchers know best practice recommendations and explanations for both conducting as well as reading/reviewing LPA models. With this paper we: 1) discuss the strengths and weaknesses of LPA and the questions it is most appropriate to answer, 2) introduce LPA conceptually, 3) illustrate an LPA conducted with exercise psychology variables following current best practice recommendations, and 4) juxtapose resulting models from the LPA approach to a typical approach with the same data. We also share the data and syntax files used to conduct the basic steps of the LPA analyses as supplemental appendix files in addition to including the tables and figures for reporting LPA results following best practices.</p>","PeriodicalId":48329,"journal":{"name":"Journal of Behavioral Medicine","volume":" ","pages":"123-137"},"PeriodicalIF":2.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13253718/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An example of identifying conditional dependence in graphical models using Bayesian model averaging: general and disease specific anxiety in a cardiovascular patient sample.","authors":"Ascher Munion, Maeve M Sargeant, Elizabeth Jordan, Erica P Barwick, Benjamin C Degner, Samuel F Sears","doi":"10.1007/s10865-026-00641-x","DOIUrl":"10.1007/s10865-026-00641-x","url":null,"abstract":"<p><p>Parsing the interplay of psychological symptoms is difficult yet crucial for both disorder conceptualization and development of targeted interventions. This study used a Bayesian Model Averaging (BMA) approach to network analysis to identify the most probable structures and to evaluate conditional dependence underlying symptomology in 110 cardiovascular patients diagnosed with a Thoracic Aortic Aneurysm (TAA). Symptoms of general and disease-specific distress were assessed using the Depression Anxiety Stress Scales (DASS-21) and the Cardiac Anxiety Questionnaire-Attention Subscale (CAQ-A). Bayesian Model Averaging (BMA) and Bayes Factors (BF) were used to inform model selection and quantify model uncertainty. We present both the psychological symptom network with the highest posterior probability—and thus the best predictive utility and the BMA network, which produces tests of conditional dependence and independence which average over all structures and thus are not conditional on the other variables present in a single selected reference model. However, the posterior distribution showed variability in plausible complexity, or the number of parameters in the network structure. Utilizing BMA is especially protective in cases of structural uncertainty and high complexity. Of the 171 possible edges, 19 edges had strong evidence for inclusion. Notably, two edges connected the general and cardiac anxiety scales: DASS_7 (worry about experiencing panic) to CAQ_3 (chest discomfort wakes me up at night) and DASS_9 (Aware of Heart Symptoms) to CAQ_2 (racing heart wakes me up at night). This emphasizes the prevalent link between physiological components contributing to both general and cardiac anxiety. This approach highlights the utility of these methods in revealing selective and specific symptom interactions and offer a set of plausible causal associations, which could inform future studies (and Directed Acyclic Graphs) oriented towards testing causal mechanisms.</p>","PeriodicalId":48329,"journal":{"name":"Journal of Behavioral Medicine","volume":" ","pages":"328-336"},"PeriodicalIF":2.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147373413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The interplay between job demands & resources and mental health: a novel approach using hidden Markov models.","authors":"Stef Bouwhuis, Ceciel Pauls, Mauricio Garnier-Villarreal, Dimitris Pavlopoulos","doi":"10.1007/s10865-025-00626-2","DOIUrl":"10.1007/s10865-025-00626-2","url":null,"abstract":"<p><p>We use a novel method (cross-lagged hidden Markov models) to identify which combinations of job demands and resources occur among workers, how often, and how these affect mental health and vice versa. Hidden Markov models (HMM) are a longitudinal extension of latent class analysis (LCA), which can be used to measure concepts that are not directly observable. As in LCA, indicator variables are used to measure such concepts. We use twelve indicators of JDR, and five indicators of mental health. HMMs group individuals with similar response patterns on the indicators in categories of the latent variable and analyse how individuals move between these categories. Additionally, predictors can be added to the model to investigate which factors influence transitions between the identified states. We used this model to study the cross-lagged relations between JDR and mental health: how JDR in time point [Formula: see text] affects mental health in time point t and mental health in time point [Formula: see text] affects JDR in time point t. We used yearly data from the Dutch Longitudinal Internet Social Survey (LISS) from 2016 to 2023. Our sample includes respondents who were employees in 2016 and for whom we had data on their JDR and mental health for at least four years. We identified six JDR states, ranging from 'Tough job' (high demands and few resources) to 'Dream job' (moderate demands and very high resources). We also identified three mental health states: poor, moderate, and good. Among those in moderate health, transitions to good health were more common for respondents in the 'Dream job' state and less common for respondents in the 'Tough job' state. Our results suggest a healthy worker effect: transitions from states with a disadvantageous combination of JDR to better states were more common among employees in moderate or good mental health. Our study shows how HMMs can improve our knowledge on the empirical predictions of widely studied theories such as the JDR model and its interplay with mental health. This is relevant for scholars and practitioners alike.</p>","PeriodicalId":48329,"journal":{"name":"Journal of Behavioral Medicine","volume":" ","pages":"176-194"},"PeriodicalIF":2.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13253657/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145999324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determining the power of a 1-sided z-test given only the power of the corresponding 2-sided test.","authors":"Amy Liang, Kristopher J Preacher, Nathaniel J Williams, Paul D Allison, Steven C Marcus, Sonya K Sterba","doi":"10.1007/s10865-025-00595-6","DOIUrl":"10.1007/s10865-025-00595-6","url":null,"abstract":"<p><p>Estimating statistical power is essential for designing behavioral medicine studies efficiently and conserving finite resources. Sometimes behavioral medicine researchers are interested in calculating power for 1-sided z-tests of individual parameters (e.g., slopes) in complex models such as multilevel structural equation models or multilevel mixture regression models. For such models, calculating power for 1-sided z-tests is cumbersome because: (a) online z-test power calculator tools are inapplicable, (b) commonly-used power analysis software provides power only for 2-sided z-tests and does not allow changing alpha, and (c) published power tables typically provide power results only for 2-sided z-tests. Hence, here we introduce straightforward and resource-efficient conversion formulas to estimate the power of 1-sided z-tests of individual parameters in any model by using direct power conversions from the corresponding 2-sided tests. We then implement these conversion formulas in accessible R and Excel software. This brief report thus provides behavioral medicine researchers with a convenient and practical solution for power calculation that minimizes the time, financial, and computational resources typically needed for power estimation.</p>","PeriodicalId":48329,"journal":{"name":"Journal of Behavioral Medicine","volume":" ","pages":"298-306"},"PeriodicalIF":2.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13253764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methods for analyzing longitudinal data from randomized pretest-posttest-follow-up trials in behavioral research: a practical guide to latent change models.","authors":"Constance A Mara","doi":"10.1007/s10865-025-00600-y","DOIUrl":"10.1007/s10865-025-00600-y","url":null,"abstract":"<p><p>Randomized pretest, posttest, follow-up (RPPF) designs are widely used in longitudinal behavioral intervention research to evaluate the efficacy of treatments over time. These designs typically involve random assignment of participants to treatment and control conditions, with assessments conducted at baseline, immediately post-intervention, and during the follow-up period. Researchers primarily focus on determining whether the intervention is more effective than the control condition at post-treatment and whether these effects are sustained or change over time. This paper presents Latent Change Models (LCMs) as a practical approach for analyzing randomized pretest-posttest-follow-up (RPPF) trials, directly estimating discrete changes between timepoints and intervention-control group differences. The utility of LCMs is demonstrated through an application to the STAR (Supporting Treatment Adherence Regimens) trial, a pediatric randomized behavioral clinical trial aimed at improving adherence to anti-seizure medications (ASMs) among children with new-onset epilepsy. The results of the trial analyzed via an LCM are contrasted with the results as analyzed by an ANCOVA, a longitudinal linear mixed-effects model, and a latent growth curve model. The tutorial and application to the STAR trial demonstrate that LCMs offer notable strengths, including the ability to estimate discrete changes over time, control for baseline variability in the outcome, and incorporate all longitudinal data within a single, parsimonious model. These models provide an accurate and nuanced understanding of intervention effects in RPPF designs, with implications for clinical intervention research.</p>","PeriodicalId":48329,"journal":{"name":"Journal of Behavioral Medicine","volume":" ","pages":"286-297"},"PeriodicalIF":2.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13253591/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling intraindividual means and variances from ecological momentary assessment data: comparing standard computational formulas to mixed-effects location-scale model estimates.","authors":"Wei-Lin Wang, Chih-Hsiang Yang, Rachel Nordgren, Jixin Li, Stephen Intille, Genevieve F Dunton, Donald Hedeker","doi":"10.1007/s10865-025-00628-0","DOIUrl":"10.1007/s10865-025-00628-0","url":null,"abstract":"<p><p>Traditionally, intraindividual means and variances derived from ecological momentary assessment (EMA) data have been calculated using standard computational formulas (SCF), such as subject-level means and standard deviations. However, these SCF methods assume uniform precision across subjects, disregarding variation in the number of observations, missing data issues, and the non-continuous nature of data scales. This study evaluated the predictive accuracy of the coefficients of intraindividual means and variances computed via SCF against those estimated using random effects from a Mixed-Effects Location Scale (MELS) model. A five-scenario simulation study was conducted: (1) varying numbers of observations per subject, (2) varying mean-to-variance ratios, (3) varying proportions of missing data under a missing completely at random (MCAR) assumption, (4) varying proportions of missing data under a missing at random (MAR) assumption, and (5) varying categories of ordinal scale responses. Bias and coverage of the mean levels and variability coefficients were compared across methods. In addition, a real-life dataset was used to compare the difference of means and variances between SCF and MELS approaches. Results consistently showed that the MELS model approach outperformed SCF method, yielding lower bias and higher coverage of the coefficients across all scenarios. These findings support the use of MELS for more accurate and reliable estimation of intraindividual means and variances in EMA data, highlighting its advantages for subsequent predictive modeling.</p>","PeriodicalId":48329,"journal":{"name":"Journal of Behavioral Medicine","volume":" ","pages":"254-274"},"PeriodicalIF":2.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13192405/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147844695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced modeling approaches for count data analysis with focus on substance use outcomes.","authors":"Niloufar Dousti Mousavi, Jie Yang, Robin Mermelstein, Donald Hedeker","doi":"10.1007/s10865-025-00610-w","DOIUrl":"10.1007/s10865-025-00610-w","url":null,"abstract":"<p><p>The selection of appropriate statistical models is essential for accurately interpreting the analysis of count data, especially in behavioral medicine. Traditionally, Poisson and Negative Binomial models have been commonly employed, but they may not always be the most optimal choices, particularly when dealing with data with an abundance of zeroes, which can be effectively modeled using zero-inflated and zero-altered (hurdle) models. Additionally, U-shaped distributions where the data are clustered around both ends-low and high counts-with fewer occurrences in the middle, cannot be adequately captured by traditional approaches and further complicate the analysis. This paper critically examines the widespread use of zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models in the context of adolescent substance use data, identifying their potential limitations. Using a dataset from a smoking study of 1263 adolescents who reported smoking behavior across eight waves, we analyzed the sparse count outcome \"Days Smoked in the Past Month,\" with covariates such as sex, age, and GPA recorded at each wave. Through a comprehensive evaluation of smoking behavior count outcomes-employing model identification via the Kolmogorov-Smirnov (KS) test, validation through confirmation studies, and regression analysis guided by Akaike Information Criterion (AIC). The range of models covered includes: ZIP, Poisson hurdle (PH), ZINB, negative binomial hurdle (NBH), zero-inflated negative binomial with fixed r (ZINB-r), negative binomial hurdle with fixed r (NBH-r), zero-inflated beta-binomial (ZIBB), beta-binomial hurdle (BBH), zero-inflated beta-binomial with fixed n (ZIBB-n), beta-binomial hurdle with fixed n (BBH-n), zero-inflated beta-binomial with fixed alpha and beta (ZIBB-ab), beta-binomial hurdle with fixed alpha and beta (BBH-ab), zero-inflated beta negative binomial (ZIBNB), and beta negative binomial hurdle (BNBH). Our study demonstrates the superior model fitting and regression analysis capabilities of the ZIBB and BBH models. Notably, our findings reveal the effectiveness of the ZIBB model in capturing the U-shaped distribution observed in real-world data. This underscores the importance of exploring a wider range of models beyond ZIP and ZINB for count data analysis. This study advocates for the broader application of these more sophisticated models in behavioral medicine, with the goal to enhance the accuracy and reliability of research outcomes.</p>","PeriodicalId":48329,"journal":{"name":"Journal of Behavioral Medicine","volume":" ","pages":"353-371"},"PeriodicalIF":2.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12973045/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145655788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing treatment effects based on the two-wave latent change score model - an alternative to repeated measures ANOVA.","authors":"Eun Seo Park, Hwa Jin Han, Young Il Cho","doi":"10.1007/s10865-025-00625-3","DOIUrl":"10.1007/s10865-025-00625-3","url":null,"abstract":"<p><p>Behavioral science and health psychology researchers often strive to investigate treatment effects using traditional statistical approaches, such as repeated measures ANOVA. However, these methods often fall short in addressing complexities like measurement error, intraindividual variability, and change processes over time. This study introduces the Two-Wave Latent Change Score Model (2W-LCSM; Henk & Castro-Schilo, 2016) as a robust alternative for modeling treatment-induced change and its long-term behavioral consequences. We demonstrate an illustrative example using data from individuals convicted of sexual crimes, incarcerated, and completing psychotherapy programs based on cognitive behavioral therapy. Our findings highlight the utility of 2W-LCSM in capturing both within-person change and its predictive relationship with recidivism. Results indicate a significant reduction in cognitive distortions post-treatment, with latent change scores emerging as a significant predictor of reduced sexual crime recidivism. These findings underscore the value of 2W-LCSM in behavioral medicine research, offering insights for tailoring interventions and advancing statistical methodologies in the field.</p>","PeriodicalId":48329,"journal":{"name":"Journal of Behavioral Medicine","volume":" ","pages":"195-206"},"PeriodicalIF":2.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145999377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}