Let their voices be heard: Understanding the barriers to seeking early prenatal care for low-income non-Hispanic Black women

Background: Receipt of early prenatal care, care during the first three months of pregnancy, is the standard in the United States. Sixty percent of non-Hispanic Black women who had a live birth in the Sunnyside community of Houston did not obtain early prenatal care in 2009. ^ This study's aims were to: 1) Describe the barriers to obtaining early prenatal care in non-Hispanic Black women who live in the Sunnyside community of Houston; and, 2) Describe the actions that could encourage non-Hispanic Black women who live in the Sunnyside Community to obtain early prenatal care. The goal was to provide information to organizations that promote early prenatal care use in non-Hispanic Black women in Harris County that may aid in developing interventions. ^ Methods: The Participatory Learning for Action rapid assessment qualitative method was used in a group setting to answer the research questions on behalf of women in the community. Women who participated in the group sessions also participated in an in-depth interview. Key informants who work in the community with pregnant women, or promote the use of prenatal care services, were also interviewed. An inductive analysis of the data was conducted to identify common themes that address the study's aims. ^ Results: Aim 1: Group participants identified fear of the reaction from family and/or the baby's daddy and shame, not having insurance or money, and lack of knowledge of the pregnancy and resources as the top three barriers to early prenatal care for women in the community. Aim 2: Group participants stated that to help women to overcome these barriers, communication, awareness and support; help, resources and services; and information and early education are needed. Participant in-depth interviewees echoed the themes of fear of the reaction from family and/or the baby's daddy and not knowing of the pregnancy. Key informants mentioned these themes as well, though not at the same priority level. Participants and key informants also mentioned similar themes for helping women to overcome barriers to early prenatal care. ^ Conclusion: A comprehensive approach is needed to improve early prenatal care use in the Sunnyside community. Education efforts must include all members of the community, young and old, to promote support for pregnant women. Community members must be a part of the process for developing education campaigns. Engaging the community builds a relationship with organizations that serve the community, which may promote use of the organizations' services, and build trust with the community. All efforts must be ongoing so that women and men of all ages in the community understand the importance of prenatal care and support women obtaining care early in the pregnancy.^

Towards Automation in Digital Investigations : Seeking Efficiency in Digital Forensics in Mobile and Cloud Environments

Cybercrime and related malicious activity in our increasingly digital world has become more prevalent and sophisticated, evading traditional security mechanisms. Digital forensics has been proposed to help investigate, understand and eventually mitigate such attacks. The practice of digital forensics, however, is still fraught with various challenges. Some of the most prominent of these challenges include the increasing amounts of data and the diversity of digital evidence sources appearing in digital investigations. Mobile devices and cloud infrastructures are an interesting specimen, as they inherently exhibit these challenging circumstances and are becoming more prevalent in digital investigations today. Additionally they embody further characteristics such as large volumes of data from multiple sources, dynamic sharing of resources, limited individual device capabilities and the presence of sensitive data. These combined set of circumstances make digital investigations in mobile and cloud environments particularly challenging. This is not aided by the fact that digital forensics today still involves manual, time consuming tasks within the processes of identifying evidence, performing evidence acquisition and correlating multiple diverse sources of evidence in the analysis phase. Furthermore, industry standard tools developed are largely evidence-oriented, have limited support for evidence integration and only automate certain precursory tasks, such as indexing and text searching. In this study, efficiency, in the form of reducing the time and human labour effort expended, is sought after in digital investigations in highly networked environments through the automation of certain activities in the digital forensic process. To this end requirements are outlined and an architecture designed for an automated system that performs digital forensics in highly networked mobile and cloud environments. Part of the remote evidence acquisition activity of this architecture is built and tested on several mobile devices in terms of speed and reliability. A method for integrating multiple diverse evidence sources in an automated manner, supporting correlation and automated reasoning is developed and tested. Finally the proposed architecture is reviewed and enhancements proposed in order to further automate the architecture by introducing decentralization particularly within the storage and processing functionality. This decentralization also improves machine to machine communication supporting several digital investigation processes enabled by the architecture through harnessing the properties of various peer-to-peer overlays. Remote evidence acquisition helps to improve the efficiency (time and effort involved) in digital investigations by removing the need for proximity to the evidence. Experiments show that a single TCP connection client-server paradigm does not offer the required scalability and reliability for remote evidence acquisition and that a multi-TCP connection paradigm is required. The automated integration, correlation and reasoning on multiple diverse evidence sources demonstrated in the experiments improves speed and reduces the human effort needed in the analysis phase by removing the need for time-consuming manual correlation. Finally, informed by published scientific literature, the proposed enhancements for further decentralizing the Live Evidence Information Aggregator (LEIA) architecture offer a platform for increased machine-to-machine communication thereby enabling automation and reducing the need for manual human intervention.

Factors influencing Hong Kong Chinese patients' decision-making in seeking early treatment for acute myocardial infarction

The purpose of this study was to identify, through in-depth interview, factors that influenced 27 Hong Kong Chinese patients' decision-making in seeking early treatment for acute myocardial infarction (AMI). The median delay time from the onset of symptoms to arrival at the hospital was 15.6 hours for men and 53.7 hours for women. Three major categories emerged from the data: (a) becoming aware of the threat, (b) maintaining a sense of normality, and (c) struggling to mobilize resources. A variety of decisions were made by patients from the onset of chest Pain to seeking help. These decisions were heavily influenced by healthcare factors (access to emergency medical service (EMS) and treatment), personal factors (cognitive interpretations of symptoms), sociocultural factors (family situation, cultural beliefs, and practices), and coping strategies. (c) 2006 Wiley Periodicals, Inc.

Client discourses on the process of seeking same-sex couple counselling

How same-sex couples manage the process of seeking help for their relationships is an under-researched area. Twelve semi-structured interviews were conducted with 16 people who had engaged in same-sex couple counselling, and were analysed using discourse analysis. The ways in which the couples positioned themselves as part of a 'minority group', or part of a generic group of couples struggling with relationship issues, impacted on how they discussed seeking help. We conclude that counsellors and psychotherapists need to be aware of the ways in which couples construct their relationships, and mindful of the tricky navigations around similarity to, and difference from, different-sex relationships. The impact of this on couples seeking therapeutic help is considered. © 2013 Taylor & Francis.

Online information seeking by patients with bipolar disorder:results from an international multisite survey

Background: Information seeking is an important coping mechanism for dealing with chronic illness. Despite a growing number of mental health websites, there is little understanding of how patients with bipolar disorder use the Internet to seek information. Methods: A 39 question, paper-based, anonymous survey, translated into 12 languages, was completed by 1222 patients in 17 countries as a convenience sample between March 2014 and January 2016. All patients had a diagnosis of bipolar disorder from a psychiatrist. Data were analyzed using descriptive statistics and generalized estimating equations to account for correlated data. Results: 976 (81 % of 1212 valid responses) of the patients used the Internet, and of these 750 (77 %) looked for information on bipolar disorder. When looking online for information, 89 % used a computer rather than a smartphone, and 79 % started with a general search engine. The primary reasons for searching were drug side effects (51 %), to learn anonymously (43 %), and for help coping (39 %). About 1/3 rated their search skills as expert, and 2/3 as basic or intermediate. 59 % preferred a website on mental illness and 33 % preferred Wikipedia. Only 20 % read or participated in online support groups. Most patients (62 %) searched a couple times a year. Online information seeking helped about 2/3 to cope (41 % of the entire sample). About 2/3 did not discuss Internet findings with their doctor. Conclusion: Online information seeking helps many patients to cope although alternative information sources remain important. Most patients do not discuss Internet findings with their doctor, and concern remains about the quality of online information especially related to prescription drugs. Patients may not rate search skills accurately, and may not understand limitations of online privacy. More patient education about online information searching is needed and physicians should recommend a few high quality websites.

Using Surface Spectroscopy to Help Interpret STM Images of Chemisorbed Molecules: Application to the Study of the Hydrogenation of Ketones and a-Ketoesters on Pt

Depuis que la haute énantiopureté est nécessaire dans l’industrie pharmaceutique, les études visant à découvrir les mécanismes pour l’hydrogénation énantiosélective de cétones ou céto-esters sur les surfaces, et à rechercher de nouveaux et plus performants catalyseurs asymétriques, sont d’une grande importance. La microscopie à effet tunnel (STM), la spectroscopie infrarouge de réflexion-absorption, la spectroscopie de désorption à température programmée et la spectrométrie de photoélectrons induits par rayons X sont des méthodes performantes facilitant la compréhension des mécanismes de réaction. En plus de nous permettre de comprendre les mécanismes réactionnels, les études peuvent fournir des informations sur la dynamique des réactions en catalyse hétérogène ainsi que sur le développement de la théorie de la fonctionnelle de la densité (DFT) afin de calculer des interactions faibles dans les processus de surface. D’autres parts, les calculs DFT fournissent une aide essentielle à l’interprétation des données de STM et spectroscopie de surface. Dans cette thèse, certains cétones et céto-esters sur la surface de platine sont étudiées par les techniques sophistiquées mentionnées ci-dessus. Mes études démontrent que la combinaison de l’utilisation de la spectroscopie de routine, des nanotechnologies et de nombreux calculs élaborés, est une méthode efficace pour étudier les réactions à la surface car ces techniques explorent les différents aspects de la surface ainsi que s’entraident mutuellement lors de certaines interprétations.

A natural model for architecture : the nature of the evolutionary model 1995

John Frazer's architectural work is inspired by living and generative processes. Both evolutionary and revolutionary, it explores informatin ecologies and the dynamics of the spaces between objects. Fuelled by an interest in the cybernetic work of Gordon Pask and Norbert Wiener, and the possibilities of the computer and the "new science" it has facilitated, Frazer and his team of collaborators have conducted a series of experiments that utilize genetic algorithms, cellular automata, emergent behaviour, complexity and feedback loops to create a truly dynamic architecture. Frazer studied at the Architectural Association (AA) in London from 1963 to 1969, and later became unit master of Diploma Unit 11 there. He was subsequently Director of Computer-Aided Design at the University of Ulter - a post he held while writing An Evolutionary Architecture in 1995 - and a lecturer at the University of Cambridge. In 1983 he co-founded Autographics Software Ltd, which pioneered microprocessor graphics. Frazer was awarded a person chair at the University of Ulster in 1984. In Frazer's hands, architecture becomes machine-readable, formally open-ended and responsive. His work as computer consultant to Cedric Price's Generator Project of 1976 (see P84)led to the development of a series of tools and processes; these have resulted in projects such as the Calbuild Kit (1985) and the Universal Constructor (1990). These subsequent computer-orientated architectural machines are makers of architectural form beyond the full control of the architect-programmer. Frazer makes much reference to the multi-celled relationships found in nature, and their ongoing morphosis in response to continually changing contextual criteria. He defines the elements that describe his evolutionary architectural model thus: "A genetic code script, rules for the development of the code, mapping of the code to a virtual model, the nature of the environment for the development of the model and, most importantly, the criteria for selection. In setting out these parameters for designing evolutionary architectures, Frazer goes beyond the usual notions of architectural beauty and aesthetics. Nevertheless his work is not without an aesthetic: some pieces are a frenzy of mad wire, while others have a modularity that is reminiscent of biological form. Algorithms form the basis of Frazer's designs. These algorithms determine a variety of formal results dependent on the nature of the information they are given. His work, therefore, is always dynamic, always evolving and always different. Designing with algorithms is also critical to other architects featured in this book, such as Marcos Novak (see p150). Frazer has made an unparalleled contribution to defining architectural possibilities for the twenty-first century, and remains an inspiration to architects seeking to create responsive environments. Architects were initially slow to pick up on the opportunities that the computer provides. These opportunities are both representational and spatial: computers can help architects draw buildings and, more importantly, they can help architects create varied spaces, both virtual and actual. Frazer's work was groundbreaking in this respect, and well before its time.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

The role of sensation seeking and need for cognition on web-site evaluations : a resource-matching perspective

The Internet theoretically enables marketers to personalize a Website to an individual consumer. This article examines optimal Website design from the perspective of personality trait theory and resource-matching theory. The influence of two traits relevant to Internet Web-site processing—sensation seeking and need for cognition— were studied in the context of resource matching and different levels of Web-site complexity. Data were collected at two points of time: personality-trait data and a laboratory experiment using constructed Web sites. Results reveal that (a) subjects prefer Web sites of a medium level of complexity, rather than high or low complexity; (b)high sensation seekers prefer complex visual designs, and low sensation seekers simple visual designs, both in Web sites of medium complexity; and (c) high need-for-cognition subjects evaluated Web sites with high verbal and low visual complexity more favourably.