The use of social media to communicate child health information to low-income parents: A formative study

The Internet, and specifically web 2.0 social media applications, offers an innovative method for communicating child health information to low-income parents. The main objective of this study was to use qualitative data to determine the value of using social media to reach low-income parents with child health information. A qualitative formative evaluation employing focus groups was used to determine the value of using social media for dissemination of child health information. Inclusion criteria included: (1) a parent with a child that attends a school in a designated Central Texas school district; and (2) English-speaking. The students who attend these schools are generally economically disadvantaged and are predominately Hispanic. The classic analysis strategy was used for data analysis. Focus group participants (n=19) were female (95%); White (53%), Hispanic (42%) or African American (5%); and received government assistance (63%). Most had access to the Internet (74%) and were likely to have low health literacy (53%). The most preferred source of child health information was the family pediatrician or general practitioner. Many participants were familiar with social media applications and had profiles on popular social networking sites, but used them infrequently. Objections to social media sites as sources of child health information included lack of credibility and parent time. Social media has excellent potential for reaching low-income parents when used as part of a multi-channel communication campaign. Further research should focus on the most effective type and format of messages that can promote behavior change in this population, such as story-telling. ^

The cost effective management of Central Line-Associated Bloodstream Infections (CLABSIs) comparing the central line bundle to antimicrobialcoated central venous catheters: A systematic review

Central Line-Associated Bloodstream Infections (CLABSIs) are one of the most costly and preventable cases of morbidity and mortality among intensive care units (ICUs) in health care today. In 2008, the Centers for Medicare and Medicaid Services Medicare Program, under the Deficit Reduction Act, announced it will no longer reimburse hospitals for such adverse events among those related to CLABSIs. This reveals the financial burden shift onto the hospital rather than the health care payer who can now withhold reimbursements. With this weighing more heavily on hospital management, decision makers will need to find a way to completely prevent cases of CLABSI or simply pay for the financial consequences. ^ To reduce the risk of CLABSIs, several clinical, preventive interventions have been studied and even instituted including the Central Line (CL) Bundle and Antimicrobial Coated Central Venous Catheters (AM-CVCs). I carried out a formal systematic review on the topic to compare the cost-effectiveness of the Central Line (CL) Bundle to the commercially available antimicrobial coated central venous catheters (AM-CVCs) in preventing CLABSIs among critically and chronically ill patients in the U.S. Evidence was assessed for inclusion against predefined criteria. I, myself, conducted the data extraction. Ten studies were included in the review. Efficacy in reducing the mean incidence rate of CLABSI by the CL Bundle and AM-CVC interventions were compared with one another including costs. ^ The AM-CVC impregnated with antibiotics, rifampin-minocycline (AI-RM) is more clinically effective than the CL Bundle in reducing the mean rate of CLABSI per 1,000 catheter days. The lowest mean incidence rate of CLABSI per 1,000 catheter days among the AM-CVC studies was as low as zero in favor of the AI-RM. Moreover, the review revealed that the AI-RM appears to be more cost-effective than the CL Bundle. Results showed the adjusted incremental cost of the CL Bundle per ICU patient requiring a CVC to be approximately $196 while the AI-RM at only an additional cost of $48 per ICU patient requiring a CVC. ^ Limited data regarding the cost of the CL Bundle made it difficult to make a true comparison to the direct cost of the AM-CVCs. However, using the result I did have from this review, I concluded that the AM-CVCs do appear to be more cost-effective in decreasing the mean rate of CLABSI while also minimizing incremental costs per CVC than the CL Bundle. This review calls for further research addressing the cost of the CL Bundle and compliance and more effective study designs such as randomized control trials comparing the efficacy and cost of the CL Bundle to the AM-CVCs. Barriers that may face health care managers when implementing the CL Bundle or AM-CVCs include additional costs associated with the intervention, educational training and ongoing reinforcement as well as creating a new culture of understanding.^

The Comprehensive Assessment of Leadership for Learning: A Next Generation Formative Evaluation and Feedback System

Recent developments in federal policy have prompted the creation of state evaluation frameworks for principals and teachers that hold educators accountable for effective practices and student outcomes. These changes have created a demand for formative evaluation instruments that reflect current accountability pressures and can be used by schools to focus school improvement and leadership development efforts. The Comprehensive Assessment of Leadership for Learning (CALL) is a next generation, 360-degree on-line assessment and feedback system that reflect best practices in feedback design. Some unique characteristics of CALL include a focus on: leadership distributed throughout the school rather than as carried out by an individual leader; assessment of leadership tasks rather than perceptions of leadership practice; a focus on larger complex systems of middle and high school; and transparency of assessment design. This paper describes research contributing to the design and validation of the CALL survey instrument.

Comparing Campus Discipline Rates: A Multivariate Approach for Identifying Schools with Significantly Different than Expected Exclusionary Discipline Rates

Campus behavior management is important for ensuring classroom order and promoting positive academic outcomes. Previous studies have shown the importance of individual student and campus personnel characteristics and campus context for explaining campus discipline rates (e.g., rates of suspension and expulsion). Assessing campus discipline rates, while controlling for these individual and campus characteristics, is important for the monitoring, evaluation, and intervention role of policymakers as well as state and federal level education agencies. Systems or metrics exist that measure other student outcomes (i.e., academic performance) with controls for individual and campus characteristics, but none exist that monitor these differences for discipline rates across campuses. In this paper, we use a multivariate model to analyze a longitudinal, statewide dataset for all secondary students in Texas from 2000 to 2008 in order to examine how campus discipline rates differ across schools with statistically similar students, teachers, and campus characteristics. The findings are important for understanding that some schools with similar characteristics have significantly different exclusionary discipline rates, and they are important for informing policy and agency level decision-making. The methodology described can easily be used by monitoring agencies as well as local school districts.

Expanding Learning Time in Schools: Considering the Challenges of Implementation and the Potential Impact

Considering the broader context of school reform that is seeking education strategies that might deliver substantial impact, this article examines four questions related to the policy and practice of expanding learning time: (a) why do educators find the standard American school calendar insufficient to meet students’ educational needs, especially those of disadvantaged students? (b) how do educators implement a longer day and/or year, addressing concerns about both educational quality and costs? (c) what does research report about outcomes of expanding time in schools? and (d) what are the future prospects for increasing the number of expanded-time schools? The paper examines these questions by considering research, policy, and practice at the national level and, throughout, by drawing upon additional evidence from Massachusetts, one of the leading states in the expanded-time movement. In considering the latter two questions, the article explores the knowns and unknowns related to expanded learning time and offers suggestions for further research.

Commentary on "Expanded Learning Time in Schools"

As co-founder of KIPP, I know from experience and research that more time in school works. A well-designed extended-time program can help underserved students catch up academically, and prepare them for the rigors of higher education. Implementing extended time more widely poses challenges, but there are also creative solutions to these challenges.

Alternative Strategies for Identifying High-Performing Charter Schools in Texas

The Obama administration's recurring policy emphasis on high-performing charter schools begs the obvious question: how do you identify a high-performing charter school? That is a crucially important policy question because any evaluation strategy that incorrectly identifies charter school performance could have negative effects on the economically and/or academically disadvantaged students who frequently attend charter schools. If low-performing schools are mislabeled and allowed to persist or encouraged to expand, then students may be harmed directly. If high-performing schools are driven from the market by misinformation, then students will lose access to programs and services that can make a difference in their lives. Most of the scholarly analysis to date has focused on comparing the performance of students in charter schools to that of similar students in traditional public schools (TPS). By design, that research measures charter school performance only in relative terms. Charter schools that outperform similarly situated, but low performing, TPSs have positive effects, even if the charter schools are mediocre in an absolute sense. This analysis describes strategies for identifying high-performing charter schools by comparing charter schools with one another. We begin by describing salient characteristics of Texas charter schools. We follow that discussion with a look at how other researchers across the country have compared charter school effectiveness with TPS effectiveness. We then present several metrics that can be used to identify high-performing charter schools. Those metrics are not mutually exclusive—one could easily justify using multiple measures to evaluate school effectiveness—but they are also not equally informative. If the goal is to measure the contributions that schools are making to student knowledge and skills, then a value-added approach like the ones highlighted in this report is clearly superior to a levels-based approach like that taken under the current accountability system.

Perceptions of institutional support of community -based participatory research within prevention research centers

The Institute of Medicine (IOM) report on the future of health care states that the focus on health needs to shift to the management and prevention of chronic illnesses and that academic health centers (AHCs) should play an active role in this process through community partnerships (IOM, 2002). Grant funding from the National Institutes of Health and the creation of the Centers for Disease Control and Prevention (CDC) Prevention Research Centers (PRC) across the county represent a transition toward more proactively seeking out community partnerships to better design and disseminate health promotion programs (Green, 2001). ^ The focus of the PRCs is to conduct rigorous, community-based, prevention research, to seek outcomes applicable to public health programs and policies. The PRCs work is to create and foster partnerships among public health and community organizations, to address health promotion and disease prevention issues (CDC, 2003). ^ The W.K. Kellogg Foundation defines CBPR as "a collaborative approach to research that equitably involves all partners in the research process and recognizes the unique strengths that each brings. CBPR begins with a research topic of importance to the community with the aim of combining knowledge and action for social change to improve community health." ^ In 1995, CDC asked the IOM to review the PRC program to examine the extent to which the program is providing the public health community with strategies to address public health problems in disease prevention and health promotion (IOM, 1997). No comprehensive evaluation n of the individual PRCs had ever been done (IOM, 1997). ^ The CDC was interested in understanding how it could better support the PRC program through improved management and oversight to influence the program's success. The CDC only represents one of the entities that influence the success of a PRC. Another key entity to consider is the support of and influence of the Schools of Public Health in which the PRCs reside. Using evaluation criteria similar to those that were developed by the IOM, this study examined how aspects of structural capacity of the Schools of Public Health in which the PRCs reside are perceived to influence PRC community-based research activities. ^

Reaching the unreached: An initiative to bridge the gap between the gap by active role of management institutes/schools

There are large numbers of business communities in India which neither had any formal education nor they took any professional training but still they contribute in successful business formation. Their presence can be felt in all areas of business. Still there is a big professional gap between the educational institutes, specially the B-Schools and this independent business community. With the help of this paper an effort is made to develop a Two-Way learning relationship for the mutual benefit of both entities. It will also highlight the role of an educational institute beyond academics for the well being of society. This may lead to derive and develop the exchange of innovative business ideas and framing the suitable policies for long term sustainability in today´s competitive arena. The study conducted by researcher with a sample size of 100 which includes a mix of well known academic professionals, MBA students and non academic business professionals has revealed that there is a need of an exchange program for the mutual benefits. There exists a big professional gap in this area which can be filled with the active and effective initiative by management institutes. An effort is made in this paper to highlight this gap and to suggest some framework to bridge the gap

The role of a firm's absorptive capacity and the technology transfer process in clusters: How effective are technology centres in low-tech clusters?

This paper analyses how the internal resources of small- and medium-sized enterprises determine access (learning processes) to technology centres (TCs) or industrial research institutes (innovation infrastructure) in traditional low-tech clusters. These interactions basically represent traded (market-based) transactions, which constitute important sources of knowledge in clusters. The paper addresses the role of TCs in low-tech clusters, and uses semi-structured interviews with 80 firms in a manufacturing cluster. The results point out that producer–user interactions are the most frequent; thus, the higher the sector knowledge-intensive base, the more likely the utilization of the available research infrastructure becomes. Conversely, the sectors with less knowledge-intensive structures, i.e. less absorptive capacity (AC), present weak linkages to TCs, as they frequently prefer to interact with suppliers, who act as transceivers of knowledge. Therefore, not all the firms in a cluster can fully exploit the available research infrastructure, and their AC moderates this engagement. In addition, the existence of TCs is not sufficient since the active role of a firm's search strategies to undertake interactions and conduct openness to available sources of knowledge is also needed. The study has implications for policymakers and academia.

Guide to intermediate band solar cell research

The intermediate band solar cell (IBSC) is a solar cell that, in order to increase its efficiency over that of single gap solar cells, takes advantage of the absorption of below-bandgap energy photons by means of an intermediate band (IB) located in the semiconductor bandgap. For this process to improve the solar cell performance, the belowbandgap photon absorption has to be effective and the IB cannot limit the open-circuit voltage of the cell. In this paper we provide a guide to the new researcher interested in the idea in order he can quickly become familiar with the concept and updated with the most relevant experimental results.

HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity

The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the ‘traditional’ set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality. This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified-easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis.

Nanoinformatics knowledge infrastructures: bringing efficient information management to nanomedical research

Nanotechnology represents an area of particular promise and significant opportunity across multiple scientific disciplines. Ongoing nanotechnology research ranges from the characterization of nanoparticles and nanomaterials to the analysis and processing of experimental data seeking correlations between nanoparticles and their functionalities and side effects. Due to their special properties, nanoparticles are suitable for cellular-level diagnostics and therapy, offering numerous applications in medicine, e.g. development of biomedical devices, tissue repair, drug delivery systems and biosensors. In nanomedicine, recent studies are producing large amounts of structural and property data, highlighting the role for computational approaches in information management. While in vitro and in vivo assays are expensive, the cost of computing is falling. Furthermore, improvements in the accuracy of computational methods (e.g. data mining, knowledge discovery, modeling and simulation) have enabled effective tools to automate the extraction, management and storage of these vast data volumes. Since this information is widely distributed, one major issue is how to locate and access data where it resides (which also poses data-sharing limitations). The novel discipline of nanoinformatics addresses the information challenges related to nanotechnology research. In this paper, we summarize the needs and challenges in the field and present an overview of extant initiatives and efforts.

Functional and effective connectivity in MEG. Application to the study of epilepsy

Nuestro cerebro contiene cerca de 1014 sinapsis neuronales. Esta enorme cantidad de conexiones proporciona un entorno ideal donde distintos grupos de neuronas se sincronizan transitoriamente para provocar la aparición de funciones cognitivas, como la percepción, el aprendizaje o el pensamiento. Comprender la organización de esta compleja red cerebral en base a datos neurofisiológicos, representa uno de los desafíos más importantes y emocionantes en el campo de la neurociencia. Se han propuesto recientemente varias medidas para evaluar cómo se comunican las diferentes partes del cerebro a diversas escalas (células individuales, columnas corticales, o áreas cerebrales). Podemos clasificarlos, según su simetría, en dos grupos: por una parte, la medidas simétricas, como la correlación, la coherencia o la sincronización de fase, que evalúan la conectividad funcional (FC); mientras que las medidas asimétricas, como la causalidad de Granger o transferencia de entropía, son capaces de detectar la dirección de la interacción, lo que denominamos conectividad efectiva (EC). En la neurociencia moderna ha aumentado el interés por el estudio de las redes funcionales cerebrales, en gran medida debido a la aparición de estos nuevos algoritmos que permiten analizar la interdependencia entre señales temporales, además de la emergente teoría de redes complejas y la introducción de técnicas novedosas, como la magnetoencefalografía (MEG), para registrar datos neurofisiológicos con gran resolución. Sin embargo, nos hallamos ante un campo novedoso que presenta aun varias cuestiones metodológicas sin resolver, algunas de las cuales trataran de abordarse en esta tesis. En primer lugar, el creciente número de aproximaciones para determinar la existencia de FC/EC entre dos o más señales temporales, junto con la complejidad matemática de las herramientas de análisis, hacen deseable organizarlas todas en un paquete software intuitivo y fácil de usar. Aquí presento HERMES (http://hermes.ctb.upm.es), una toolbox en MatlabR, diseñada precisamente con este fin. Creo que esta herramienta será de gran ayuda para todos aquellos investigadores que trabajen en el campo emergente del análisis de conectividad cerebral y supondrá un gran valor para la comunidad científica. La segunda cuestión practica que se aborda es el estudio de la sensibilidad a las fuentes cerebrales profundas a través de dos tipos de sensores MEG: gradiómetros planares y magnetómetros, esta aproximación además se combina con un enfoque metodológico, utilizando dos índices de sincronización de fase: phase locking value (PLV) y phase lag index (PLI), este ultimo menos sensible a efecto la conducción volumen. Por lo tanto, se compara su comportamiento al estudiar las redes cerebrales, obteniendo que magnetómetros y PLV presentan, respectivamente, redes más densamente conectadas que gradiómetros planares y PLI, por los valores artificiales que crea el problema de la conducción de volumen. Sin embargo, cuando se trata de caracterizar redes epilépticas, el PLV ofrece mejores resultados, debido a la gran dispersión de las redes obtenidas con PLI. El análisis de redes complejas ha proporcionado nuevos conceptos que mejoran caracterización de la interacción de sistemas dinámicos. Se considera que una red está compuesta por nodos, que simbolizan sistemas, cuyas interacciones se representan por enlaces, y su comportamiento y topología puede caracterizarse por un elevado número de medidas. Existe evidencia teórica y empírica de que muchas de ellas están fuertemente correlacionadas entre sí. Por lo tanto, se ha conseguido seleccionar un pequeño grupo que caracteriza eficazmente estas redes, y condensa la información redundante. Para el análisis de redes funcionales, la selección de un umbral adecuado para decidir si un determinado valor de conectividad de la matriz de FC es significativo y debe ser incluido para un análisis posterior, se convierte en un paso crucial. En esta tesis, se han obtenido resultados más precisos al utilizar un test de subrogadas, basado en los datos, para evaluar individualmente cada uno de los enlaces, que al establecer a priori un umbral fijo para la densidad de conexiones. Finalmente, todas estas cuestiones se han aplicado al estudio de la epilepsia, caso práctico en el que se analizan las redes funcionales MEG, en estado de reposo, de dos grupos de pacientes epilépticos (generalizada idiopática y focal frontal) en comparación con sujetos control sanos. La epilepsia es uno de los trastornos neurológicos más comunes, con más de 55 millones de afectados en el mundo. Esta enfermedad se caracteriza por la predisposición a generar ataques epilépticos de actividad neuronal anormal y excesiva o bien síncrona, y por tanto, es el escenario perfecto para este tipo de análisis al tiempo que presenta un gran interés tanto desde el punto de vista clínico como de investigación. Los resultados manifiestan alteraciones especificas en la conectividad y un cambio en la topología de las redes en cerebros epilépticos, desplazando la importancia del ‘foco’ a la ‘red’, enfoque que va adquiriendo relevancia en las investigaciones recientes sobre epilepsia. ABSTRACT There are about 1014 neuronal synapses in the human brain. This huge number of connections provides the substrate for neuronal ensembles to become transiently synchronized, producing the emergence of cognitive functions such as perception, learning or thinking. Understanding the complex brain network organization on the basis of neuroimaging data represents one of the most important and exciting challenges for systems neuroscience. Several measures have been recently proposed to evaluate at various scales (single cells, cortical columns, or brain areas) how the different parts of the brain communicate. We can classify them, according to their symmetry, into two groups: symmetric measures, such as correlation, coherence or phase synchronization indexes, evaluate functional connectivity (FC); and on the other hand, the asymmetric ones, such as Granger causality or transfer entropy, are able to detect effective connectivity (EC) revealing the direction of the interaction. In modern neurosciences, the interest in functional brain networks has increased strongly with the onset of new algorithms to study interdependence between time series, the advent of modern complex network theory and the introduction of powerful techniques to record neurophysiological data, such as magnetoencephalography (MEG). However, when analyzing neurophysiological data with this approach several questions arise. In this thesis, I intend to tackle some of the practical open problems in the field. First of all, the increase in the number of time series analysis algorithms to study brain FC/EC, along with their mathematical complexity, creates the necessity of arranging them into a single, unified toolbox that allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of them. I developed such a toolbox for this aim, it is named HERMES (http://hermes.ctb.upm.es), and encompasses several of the most common indexes for the assessment of FC and EC running for MatlabR environment. I believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis and will entail a great value for the scientific community. The second important practical issue tackled in this thesis is the evaluation of the sensitivity to deep brain sources of two different MEG sensors: planar gradiometers and magnetometers, in combination with the related methodological approach, using two phase synchronization indexes: phase locking value (PLV) y phase lag index (PLI), the latter one being less sensitive to volume conduction effect. Thus, I compared their performance when studying brain networks, obtaining that magnetometer sensors and PLV presented higher artificial values as compared with planar gradiometers and PLI respectively. However, when it came to characterize epileptic networks it was the PLV which gives better results, as PLI FC networks where very sparse. Complex network analysis has provided new concepts which improved characterization of interacting dynamical systems. With this background, networks could be considered composed of nodes, symbolizing systems, whose interactions with each other are represented by edges. A growing number of network measures is been applied in network analysis. However, there is theoretical and empirical evidence that many of these indexes are strongly correlated with each other. Therefore, in this thesis I reduced them to a small set, which could more efficiently characterize networks. Within this framework, selecting an appropriate threshold to decide whether a certain connectivity value of the FC matrix is significant and should be included in the network analysis becomes a crucial step, in this thesis, I used the surrogate data tests to make an individual data-driven evaluation of each of the edges significance and confirmed more accurate results than when just setting to a fixed value the density of connections. All these methodologies were applied to the study of epilepsy, analysing resting state MEG functional networks, in two groups of epileptic patients (generalized and focal epilepsy) that were compared to matching control subjects. Epilepsy is one of the most common neurological disorders, with more than 55 million people affected worldwide, characterized by its predisposition to generate epileptic seizures of abnormal excessive or synchronous neuronal activity, and thus, this scenario and analysis, present a great interest from both the clinical and the research perspective. Results revealed specific disruptions in connectivity and network topology and evidenced that networks’ topology is changed in epileptic brains, supporting the shift from ‘focus’ to ‘networks’ which is gaining importance in modern epilepsy research.

HERMES: towards an integrated toolbox to characterize functional and effective brain connectivity

The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the ?traditional? set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality. This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified, easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis.