Scaling up research on discrimination and health: The abridged Explicit Discrimination Scale

2021 ◽  
pp. 135910532110188
Author(s):  
João L Bastos ◽  
Michael E Reichenheim ◽  
Yin C Paradies
Keyword(s):  
Undergraduate Students ◽  
Large Scale ◽  
Perceived Discrimination ◽  
Latent Trait ◽  
Scaling Up ◽  
Health Impacts ◽  
Scale Scores ◽  
Scalar Structure ◽  
Using Data

Using data from two studies conducted among diverse undergraduate students, we assessed the scalar structure of the Explicit Discrimination Scale (EDS), and developed an abridged version of the instrument. Our findings suggest that the EDS has acceptable scalability properties, including an adequate dispersion of items along the latent trait continuum. Results also support the idea that increasing raw scale scores reflect higher intensities of perceived discrimination. This shortened version of the EDS may be used in large-scale studies on the health impacts of discrimination.

scholarly journals The Psychogeriatric Assessment Scales: a multidimensional alternative to categorical diagnoses of dementia and depression in the elderly

1995 ◽  
Vol 25 (3) ◽  
pp. 447-460 ◽  
Author(s):  
A. F. Jorm ◽  
A. J. Mackinnon ◽  
A. S. Henderson ◽  
R. Scott ◽  
H. Christensen ◽  
...  
Keyword(s):  
Latent Trait ◽  
Population Sample ◽  
Reliability And Validity ◽  
The Elderly ◽  
Clinical Samples ◽  
Scale Scores ◽  
Clinical Diagnoses ◽  
Using Data ◽  
Clinical Changes ◽  
Assessment Scales

SYNOPSISThe Psychogeriatric Assessment Scales (PAS) provide an assessment of the clinical changes seen in dementia and depression. Principal components analysis and latent trait analysis were used to develop a set of scales to summarize these clinical changes. There are three scales derived from an interview with the subject (Cognitive Impairment, Depression, Stroke) and three from an interview with an informant (Cognitive Decline, Behaviour Change, Stroke). Results are reported on the reliability and validity of these scales using data from clinical samples in Sydney and Geneva and a population sample from Canberra. The scales were found to have excellent validity when judged against clinical diagnoses of dementia and depression and could distinguish Alzheimer's from vascular dementia. Cut-off points were developed to indicate correspondence between scale scores and clinical diagnoses. Percentile rank norms were developed from the Canberra population sample. The PAS is easy to administer and score and can be used by lay interviewers after training. It is intended for application both in research and in services for the elderly.

scholarly journals A Pandemic of Busywork: Increased Online Coursework Following the Transition to Remote Instruction is Associated with Reduced Academic Achievement

2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Benjamin A Motz ◽  
Joshua D Quick ◽  
Julie A Wernert ◽  
Tonya A Miles
Keyword(s):  
Academic Achievement ◽  
Online Learning ◽  
Undergraduate Students ◽  
Large Scale ◽  
Learning Activities ◽  
Course Performance ◽  
Course Materials ◽  
Show Evidence ◽  
Improved Learning ◽  
Using Data

Under normal circumstances, when students invest more effort in their schoolwork, they generally show evidence of improved academic achievement.  But when universities abruptly transitioned to remote instruction in Spring 2020, instructors assigned rapidly-prepared online learning activities, disrupting the normal relationship between effort and outcomes.  In this study, we examine this relationship using data observed from a large-scale survey of undergraduate students, from logs of student activity in the online learning management system, and from students’ estimated cumulative performance in their courses (n = 4,636).  We find that there was a general increase in the number of assignments that students were expected to complete following the transition to remote instruction, and that students who spent more time and reported more effort carrying out this coursework generally had lower course performance and reported feeling less successful.  We infer that instructors, under pressure to rapidly put their course materials online, modified their courses to include online busywork that did not constitute meaningful learning activities, which had a detrimental effect on student outcomes at scale.  These findings are discussed in contrast with other situations when increased engagement does not necessarily lead to improved learning outcomes, and in comparison with the broader relationship between effort and academic achievement.

The Effect of Using Data Analytical Capabilities to Enhance the Performance of Undergraduate Students

2019 ◽  
Author(s):  
Mohammed Q. Shatnawi ◽  
Maen J. Qaddoura ◽  
Qusai Abuein ◽  
Zain Halloush
Keyword(s):  
Undergraduate Students ◽  
Using Data

Student Perceptions of Community: A Different Perspective

NASPA Journal ◽  
1998 ◽  
Vol 35 (4) ◽  
Author(s):  
Jackie Clark ◽  
Joan Hirt
Keyword(s):  
Student Perceptions ◽  
Large Scale ◽  
Research University ◽  
Small Scale ◽  
Social Environments ◽  
Common Assumption ◽  
Small Communities ◽  
Public Research University ◽  
The Common ◽  
Using Data

The creation of small communities has been proposed as a way of enhancing the educational experience of students at large institutions. Using data from a survey of students living in large and small residences at a public research university, this study does not support the common assumption that small-scale social environments are more conducive to positive community life than large-scale social environments.

The validity of diagnostic systems for common mental disorders: a comparison between the ID-CATEGO and the DSM-III systems

1990 ◽  
Vol 20 (1) ◽  
pp. 209-218 ◽  
Author(s):  
David Grayson ◽  
Keith Bridges ◽  
Diane Cook ◽  
David Goldberg
Keyword(s):  
Discriminant Analysis ◽  
Depressive Disorders ◽  
Common Mental Disorders ◽  
Latent Trait ◽  
Mental Illnesses ◽  
Anxiety And Depression ◽  
Multiple Discriminant Analysis ◽  
Diagnostic Systems ◽  
Scale Scores ◽  
Initial Scale

SYNOPSISIt is argued that latent trait analysis provides a way of examining the construct validity of diagnostic concepts which are used to categorize common mental illnesses. The present study adds two additional aspects of validity using multiple discriminant analysis applied to two widely used taxonomic systems. Scales of anxiety and depression derived from previous latent trait analyses are applied to individuals reaching criteria for ‘caseness’ on the ID-CATEGO system and the DSM-III system, both at initial diagnosis and six months later. The first multiple discriminant analysis is carried out on the initial scale scores, and the results are interpreted in terms of concurrent validity. The second analysis uses improvement scores on the two scales and relates to predictive validity. It is argued that the ID-CATEGO system provides a better classification for common mental illnesses than the DSM-III system, since it allows a better discrimination to be made between anxiety and depressive disorders.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

scholarly journals Nebula: ultra-efficient mapping-free structural variant genotyper

2021 ◽  
Author(s):  
Parsoa Khorsand ◽  
Fereydoun Hormozdiari
Keyword(s):  
Large Scale ◽  
Structural Variants ◽  
Sequencing Technologies ◽  
Generic Framework ◽  
Common Genetic Variants ◽  
Order Of Magnitude ◽  
Complex Events ◽  
Comparable Accuracy ◽  
Using Data ◽  
Computational Resources

Abstract Large scale catalogs of common genetic variants (including indels and structural variants) are being created using data from second and third generation whole-genome sequencing technologies. However, the genotyping of these variants in newly sequenced samples is a nontrivial task that requires extensive computational resources. Furthermore, current approaches are mostly limited to only specific types of variants and are generally prone to various errors and ambiguities when genotyping complex events. We are proposing an ultra-efficient approach for genotyping any type of structural variation that is not limited by the shortcomings and complexities of current mapping-based approaches. Our method Nebula utilizes the changes in the count of k-mers to predict the genotype of structural variants. We have shown that not only Nebula is an order of magnitude faster than mapping based approaches for genotyping structural variants, but also has comparable accuracy to state-of-the-art approaches. Furthermore, Nebula is a generic framework not limited to any specific type of event. Nebula is publicly available at https://github.com/Parsoa/Nebula.

scholarly journals Perceived discrimination and psychological distress among immigrants to Canada: The mediating role of bicultural identity orientations

2021 ◽  
pp. 136843022199008
Author(s):  
Mustafa Firat ◽  
Kimberly A. Noels
Keyword(s):  
Psychological Distress ◽  
Undergraduate Students ◽  
Structural Equation ◽  
Perceived Discrimination ◽  
The Other ◽  
Equation Modeling ◽  
Bicultural Identity ◽  
Cross Sectional ◽  
Mediating Role ◽  
The Relationship

Bicultural identity orientations have rarely been examined in relation to both perceived discrimination and psychological distress. Furthermore, these constructs have usually been studied in isolation, but their intersection is essential for understanding intercultural relations in multicultural societies. Using cross-sectional data from 1,143 Canadian undergraduate students from immigrant families, this study explored the relationship between perceived discrimination and psychological distress, and how bicultural identity orientations might mediate this relationship. The structural equation modeling results indicated that perceived discrimination was associated with higher levels of psychological distress and hybrid, monocultural, alternating, and conflicted orientations, but lower levels of complementary orientation. Alternating and conflicted orientations were related to higher psychological distress, whereas the other orientations were not. Alternating and conflicted orientations mediated the relationship between perceived discrimination and psychological distress, whereas the other orientations did not. The findings are discussed in light of theories on identity integration, rejection–identification, and acculturation.

scholarly journals Assisting scalable diagnosis automatically via CT images in the combat against COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bohan Liu ◽  
Pan Liu ◽  
Lutao Dai ◽  
Yanlin Yang ◽  
Peng Xie ◽  
...  
Keyword(s):  
Large Scale ◽  
Area Under The Curve ◽  
Independent Set ◽  
Reference Method ◽  
Ct Images ◽  
Loss Of Life ◽  
Case Identification ◽  
Reverse Transcription Pcr ◽  
Using Data

AbstractThe pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

scholarly journals Integrative cBioPortal Analysis Revealed Molecular Mechanisms That Regulate EGFR-PI3K-AKT-mTOR Pathway in Diffuse Gliomas of the Brain

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3247
Author(s):  
Petar Brlek ◽  
Anja Kafka ◽  
Anja Bukovac ◽  
Nives Pećina-Šlaus
Keyword(s):  
Large Scale ◽  
Molecular Mechanisms ◽  
In Silico Analysis ◽  
Total Sample ◽  
Mtor Signaling ◽  
Biological Behavior ◽  
Mrna Transcription ◽  
Diffuse Gliomas ◽  
New Treatment ◽  
Using Data

Diffuse gliomas are a heterogeneous group of tumors with aggressive biological behavior and a lack of effective treatment methods. Despite new molecular findings, the differences between pathohistological types still require better understanding. In this in silico analysis, we investigated AKT1, AKT2, AKT3, CHUK, GSK3β, EGFR, PTEN, and PIK3AP1 as participants of EGFR-PI3K-AKT-mTOR signaling using data from the publicly available cBioPortal platform. Integrative large-scale analyses investigated changes in copy number aberrations (CNA), methylation, mRNA transcription and protein expression within 751 samples of diffuse astrocytomas, anaplastic astrocytomas and glioblastomas. The study showed a significant percentage of CNA in PTEN (76%), PIK3AP1 and CHUK (75% each), EGFR (74%), AKT2 (39%), AKT1 (32%), AKT3 (19%) and GSK3β (18%) in the total sample. Comprehensive statistical analyses show how genomics and epigenomics affect the expression of examined genes differently across various pathohistological types and grades, suggesting that genes AKT3, CHUK and PTEN behave like tumor suppressors, while AKT1, AKT2, EGFR, and PIK3AP1 show oncogenic behavior and are involved in enhanced activity of the EGFR-PI3K-AKT-mTOR signaling pathway. Our findings contribute to the knowledge of the molecular differences between pathohistological types and ultimately offer the possibility of new treatment targets and personalized therapies in patients with diffuse gliomas.

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