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The purpose of this chapter was to develop a framework for comprehensive business diagnostics, which can be integrated into the management of small and medium-sized businesses.

The chapter proposes a multiloop model of comprehensive business diagnostics, which provides the company with timely and objective information about the dynamics of its key performance indicators. The model allows enterprises of the real sector to optimize the information acquiring process with the help of the express diagnostics indicators and ensure the high quality and efficiency of the obtained information. Testing of the multiloop model at the Pavlovo Posad Shawl Manufactory JSC involved three stages: express diagnostics (ED), general diagnostics (GD), and comprehensive diagnostics (CD).

The results determined the need to move to the comprehensive diagnostics (CD) loop of the model. The approbation of the suggested model at the shawl manufacture, particularly the CD loop, allowed the authors to identify the key factors influencing the company's main economic and financial indicators.

The proposed multiloop model can be used at enterprises with different forms of ownership, including small and medium-sized businesses, both in Russia and abroad.

A multiloop model of comprehensive business diagnostics provides managerial staff with qualitative real-time information that helps them make informed and effective management decisions.

The proposed multiloop model allows businesses to acquire data about the dynamics of key indicators, determine the factors' impacts on key financial and economic indicators, and ensure high efficiency of management decisions with minimal cost.

Multiple sclerosis (MS) is a chronic condition with variable physical, cognitive, and quality of life impacts. Little research has investigated how MS outcomes vary by social identity (race, gender, disability, age, sexual orientation, and nationality) and social location (place within systems of power and privilege). However, emerging evidence points to racial and ethnic group disparities in MS outcomes. This chapter integrates core concepts from the life course perspective and an intersectional feminist disability framework to interrogate the role of diagnosis pathways in determining differential MS outcomes. MS diagnosis pathways (the time from symptom onset to the point of diagnosis) are a logical place to begin this work given the varying nature of symptom onset and the importance of a quick diagnosis for optimal MS outcomes. Whereas the life course perspective provides a framework for understanding disability transitions and pathways across the life span, an intersectional feminist disability framework centers disability within an axis of overlapping social identities and locations. The combination of both frameworks provides an approach capable of examining how MS disparities and inequities emerge in different contexts over time. The chapter begins with an overview of MS and current knowledge on disparities (mainly racial) in MS prevalence, diagnosis, and outcomes. The chapter proceeds to describe the utility of key concepts of both the life course perspective and intersectional frameworks when researching health disparities. Finally, the chapter ends with a theoretical application of an intersectional feminist disability life course perspective to investigate disparities in MS diagnosis pathways.

Post-traumatic stress disorder (PTSD) affects both civilian and military populations following wartime experiences. However, despite an abundance of research investigating civilian and military populations separately, much less focus has been given to synthesizing and integrating findings to describe how civilian and military war survivors are comparatively affected by PTSD. This review is broken down into three sections covering (1) risk factors associated with PTSD, (2) relationships between PTSD and mental health outcomes, and (3) protective factors that can attenuate PTSD and its effects. Each section covers findings for civilians and military personnel and highlights similarities and differences between groups.

Purpose – In this chapter, I examine the ways that various trauma carriers, including social movements, self-identified survivors, professional organizations, and advocates make public claims about trauma and the PTSD diagnosis as they work to define moral and political issues.

Methodology/approach – Employing the method of social pattern analysis, I analyze a variety of narrative data pertaining to issues such as child sexual abuse, war, slavery, and genocide.

Findings – Trauma carriers engage in significant social memory work and collective identity work, define social problems, and practice social activism as they address the causes and consequences of psychological suffering. Within the context of modern diagnostic psychiatry, the PTSD diagnosis stands out as a unique narrative of social illness. The PTSD diagnosis is a powerful cultural script that various individuals and interest groups use to interpret mental health symptoms while attributing psychological consequences to social causes as opposed to problems rooted in the individual's psyche (as with psychoanalysis) or neurophysiology (as with modern diagnostic psychiatry). By implication, the social world must be “cured” for the individual to be healthy.

Originality/value of paper – I detail the unique sociocognitive implications of the PTSD diagnosis, highlighting its impact on our collective understanding of particular traumatic experiences and the shared nature of posttraumatic affect. I show the relevance of social memory studies, the more broadly conceived sociology of culture and cognition – especially as it pertains to collective identity and classification norms, the sociology of health-focused social movements, and the analysis of social problems claims-making to an emerging sociology of diagnosis.

Estimates of the prevalence of AS in children throughout the entire population of the United States are highly limited and greatly variable. Ozonoff, Dawson, and McPartland (2002) stated that estimates of AS range from 0.2 to 0.5% (or 2–5 individuals in 1,000), while Volkmar and Klin (2000) cited studies reporting rates of 36 in 1,000 to approximately 1 in 10,000. The Diagnostic and Statistical Manual of the American Psychiatric Association (2000), fourth edition (DSM-IV-TR), states that “definitive data about the prevalence of Asperger Syndrome does not exist.”

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

Purpose – This chapter discusses the proposed changes in the Diagnostic and Statistical Manual of Mental Disorders (DSM-V), which eliminates Asperger's disorder (AD) and replaces it as “autism spectrum disorder.” Implications of these changes on the identity of adults with AD and the influence of everyday life experiences will be addressed.

Methodology/approach – This research is based on 19 interviews with adults diagnosed or self-diagnosed with AD. Central themes surrounding issues of identity and everyday life experiences were determined using grounded theory approaches.

Findings – This study demonstrates how the diagnosis and self-diagnosis of AD is fused with individual identity. It also shows how Asperger identity is positively embraced. The proposed changes to eliminate AD in DSM-V threaten these assertions of Asperger identity, which could potentially enhance stigma experienced by people with AD. Regardless of its removal, Asperger identity must be considered within the broader context of people's everyday lives and how experiences in social interaction and communication can be strong agents of identity construction.

Social implications – The proposed changes to eliminate AD in DSM-V is a social issue that will impact individuals with Asperger's and their families, as well as health-care professionals, health insurers, researchers, state agencies, and educational providers.

Originality/value of paper – This chapter offers a unique insight into identity construction based on the diagnosis and self-diagnosis of AD.

Child psychiatrist Leo Kanner (pronounced “Konner;” Feinstein, 2010, p. 19) published a ground-breaking paper in 1943 that introduced the world to the present-day concept of autism (Fombonne, 2003; Goldstein & Ozonoff, 2009; Roth, 2010). Prior to Kanner, however, several physicians described the condition of autism without identifying it as such. A textbook published in 1809, titled Observations on Madness and Melancholy, contained a description of a boy whose symptoms fit the modern definition of autism (Feinstein, 2010; Vaillant, 1962). The book's author, Dr. John Haslam, wrote about a 5-year-old male who was admitted to the Bethlem Asylum in 1799 with a medical history that included a case of measles when he was 1 year old. The boy's mother claimed that at age 2 years, her son became harder to control. She also indicated that he did not begin to walk until he was 2½ years of age and did not talk until he was 4 years old. Once hospitalized, the boy cried only briefly upon separation from his mother and was “constantly in action” (Vaillant, 1962, p. 376), suggesting that he was hyperactive. Hyperactivity is a characteristic commonly found in children with ASDs (APA, 2000; Wicks-Nelson & Israel, 2009). Although this child watched other boys at play in the hospital, he never joined them and played intently with toy soldiers by himself. The boy could not learn to read and always referred to himself in the third person (Vaillant, 1962). Grammatical errors in speech can be observed among individuals with ASDs (Roth, 2010; Wicks-Nelson & Israel, 2009).

With the advent of Big Data, the ability to store and use the unprecedented amount of clinical information is now feasible via Electronic Health Records (EHRs). The massive collection of clinical data by health care systems and treatment canters can be productively used to perform predictive analytics on treatment plans to improve patient health outcomes. These massive data sets have stimulated opportunities to adapt computational algorithms to track and identify target areas for quality improvement in health care.

According to a report from Association of American Medical Colleges, there will be an alarming gap between demand and supply of health care work force in near future. The projections show that, by 2032 there is will be a shortfall of between 46,900 and 121,900 physicians in US (AAMC, 2019). Therefore, early prediction of health care risks is a demanding requirement to improve health care quality and reduce health care costs. Predictive analytics uses historical data and algorithms based on either statistics or machine learning to develop predictive models that capture important trends. These models have the ability to predict the likelihood of the future events. Predictive models developed using supervised machine learning approaches are commonly applied for various health care problems such as disease diagnosis, treatment selection, and treatment personalization.

This chapter provides an overview of various machine learning and statistical techniques for developing predictive models. Case examples from the extant literature are provided to illustrate the role of predictive modeling in health care research. Together with adaptation of these predictive modeling techniques with Big Data analytics underscores the need for standardization and transparency while recognizing the opportunities and challenges ahead.

Heritability studies attempt to estimate the contribution of genes (vs. environments) to variation in phenotypes (or outcomes of interest) in a given population at a given time. This chapter scrutinizes heritability studies of adverse health phenotypes, emphasizing flaws that have become more glaring in light of recent advances in the life sciences and manifest most visibly in epigenetics.

Drawing on a diverse body of research and critical scholarship, this chapter examines the veracity of methodological and conceptual assumptions of heritability studies.

The chapter argues that heritability studies are futile for two reasons: (1) heritability studies suffer from serious methodological flaws with the overall effect of making estimates inaccurate and likely biased toward inflated heritability, and, more importantly (2) the conceptual (biological) model on which heritability studies depend – that of identifiably separate effects of genes versus the environment on phenotype variance – is unsound. As discussed, contemporary bioscientific work indicates that genes and environments are enmeshed in a complex (bidirectional, interactional), dynamic relationship that defies any attempt to demarcate separate contributions to phenotype variance. Thus, heritability studies attempt the biologically impossible. The emerging research on the importance of microbiota is also discussed, including how the commensal relationship between microbial and human cells further stymies heritability studies.

Understandably, few sociologists have the time or interest to be informed about the methodological and theoretical underpinnings of heritability studies or to keep pace with the incredible advances in genetics and epigenetics over the last several years. The present chapter aims to provide interested scholars with information about heritability and heritability estimates of adverse health outcomes in light of recent advances in the biosciences.