Individuals with dementia show a progressive decline in cognitive functioning which results in an inability to complete activities of daily... Show moreIndividuals with dementia show a progressive decline in cognitive functioning which results in an inability to complete activities of daily living (American Psychiatric Association, 2013). Early diagnosis of dementia is a positive prognostic indicator (World Alzheimer Report, 2011) and is widely regarded as an important pre-condition for improving dementia care (Kim et al., 2015; Vernooij-Dassen et al., 2005). However, negative attitudes and stigma towards dementia could possibly interfere with an individual’s willingness to recognize or accept the idea of themselves having the disease through label avoidance. The goal of the present study was to contribute to understanding the perception of dementia by developing a quantitatively derived and psychometrically validated measure that encompasses the positive and negative attitudes towards dementia held by people without dementia. This study also explored the potential association between negative attitudes about dementia and lack of familiarity with dementia as familiarity with individuals with mental illness is related to stigmatizing attitudes towards mental illness. These goals were achieved by a principal components analysis (PCA) of 56 modified items from extant and well-validated mental illness attitude scales (Community Attitudes to Mental Illness, CAMI, Taylor & Dear, 1981; Social Distance Scale, SDS, Link, 1986; Depression Stigma Scale, DSS, Griffiths et al., 2004). Convergent validity was assessed by examining the relationship between the final derived measure and a construct associated with negative attitudes about mental illness (Mental Retardation Attitude Inventory-Revised, MRAI-R). Discriminant validity was assessed by examining the relationship between the final measure and a construct that should be unrelated to negative attitudes about mental illness (Belief in a Just World Scale, BJW). Finally, exploratory analyses were conducted to assess if attitudes measured by the newly created scale are related to participants’ familiarity with dementia (Level of Familiarity Scale, LoFS, Corrigan et al., 2001).
400 adults with no history of dementia were recruited through Amazon’s MTurk. Participants were compensated by a credit to their Amazon account upon completion of the survey.
The PCA supported 2 conceptually different (not method variance) latent components titled Negative Attitudes and Positive Attitudes. These 2 components comprise the Attitudes to Dementia Inventory (ADI). Construct validity was partially supported for each component of the ADI. Degree of familiarity with dementia was not associated with negative or positive attitudes about dementia.
Overall, this study is an important contribution to dementia attitudes research. Given the identification of Negative Attitudes and Positive Attitudes have been identified as distinct dimensions of dementia attitudes, the ADI can be used to further investigate how negative reactions towards dementia might cause delays in initiating medical intervention and treatment, and also to examine whether positive attitudes provide any protections against the probable effects of negative attitudes on stigma and help-seeking behaviors. Since the early recognition and diagnosis of dementia is widely regarded as an important condition for improving dementia care (Kim et al., 2015; Vernooij-Dassen, et al., 2005), the ADI can be used to inform stigma-prevention, which hopefully translates into improved help-seeking behaviors. Show less
Artificial Intelligence (AI) holds a great promise in the healthcare. It provides a variety of advantages with its application in clinical... Show moreArtificial Intelligence (AI) holds a great promise in the healthcare. It provides a variety of advantages with its application in clinical diagnosis, disease prediction, and treatment, with such interests intensifying in the medical image field. AI can automate various cumbersome data processing techniques in medical imaging such as segmentation of left ventricular chambers and image-based classification of diseases. However, full clinical implementation and adaptation of emerging AI-based tools face challenges due to the inherently opaque nature of such AI algorithms based on Deep Neural Networks (DNN), for which computer-trained bias is not only difficult to detect by physician users but is also difficult to safely design in software development. In this work, we examine AI application in Cardiac Magnetic Resonance (CMR) using an automated image classification task, and thereby propose an AI quality control framework design that differentially evaluates the black-box DNN via carefully prepared input data with shape and fidelity variations to probe system responses to these variations. Two variants of the Visual Geometric Graphics with 19 neural layers (VGG19) was used for classification, with a total of 60,000 CMR images. Findings from this work provides insights on the importance of quality training data preparation and demonstrates the importance of data shape variability. It also provides gateway for computation performance optimization in training and validation time. Show less
This study aimed to verify that whether a low-coverage genome can work as an effective approach to isolate Lepidopteran microsatellites. As... Show moreThis study aimed to verify that whether a low-coverage genome can work as an effective approach to isolate Lepidopteran microsatellites. As microsatellites are useful tool to study population genetics, and there are many Lepidopteran agriculture pests which can cause huge economic damages every year, additionally, Lepidoptera have abundant similar flanking sequences making it difficult to develop reliable microsatellites. However, there are not enough published genomes of Lepidoptera species. If low-coverage Lepidopteran genomes can be used to isolate reliable microsatellites, the low-coverage genomes would be an effective and economical approach for microsatellites isolation, because low-coverage genome sequencing is much cheaper and less time-consuming than the published genome sequencing. Show less
Photograph of the Aaron Galleries Booth at the Art 20 exhibition, at Park Place Armory in 2006, including Mary Henry's painting The Chelsea... Show morePhotograph of the Aaron Galleries Booth at the Art 20 exhibition, at Park Place Armory in 2006, including Mary Henry's painting The Chelsea Way visible at center. Inscription on verso: "Art 20 - Park Ave. Armory 2006 Mary Henry 'The Chelsea Way' on the aisle Aaron Galleries Booth." Show less
Photograph of the Aaron Galleries Booth at the Art 20 exhibition, at Park Place Armory in 2006, including Mary Henry's painting The Chelsea... Show morePhotograph of the Aaron Galleries Booth at the Art 20 exhibition, at Park Place Armory in 2006, including Mary Henry's painting The Chelsea Way visible at center right. Inscription on verso: "Art 20 - Park Ave. Armory 2006 Mary Henry 'The Chelsea Way' on the aisle Aaron Galleries Booth." Show less
Photograph of the Aaron Galleries Booth at the Art 20 exhibition, at Park Place Armory in 2006, including Mary Henry's painting The Chelsea... Show morePhotograph of the Aaron Galleries Booth at the Art 20 exhibition, at Park Place Armory in 2006, including Mary Henry's painting The Chelsea Way visible at right. Inscription on verso: "Art 20 - Park Ave. Armory 2006 Mary Henry 'The Chelsea Way' on the aisle Aaron Galleries Booth." Show less