Attitudes toward disabilities among students in college settings: A multidimensional scaling analysis with biplot

2019 ◽  
Vol 25 (2) ◽  
pp. 79-95
Author(s):  
Wei-Mo Tu ◽  
Min-Chi Yan ◽  
Qiwei Li ◽  
Justin Watts
Keyword(s):  
Anxiety Disorder ◽  
Major Depression ◽  
Multidimensional Scaling ◽  
Hierarchical Cluster ◽  
Autism Spectrum ◽  
Attitudes Toward Disabilities ◽  
Scaling Analysis ◽  
Cord Injury ◽  
Dimension Space ◽  
Perceived Dangerousness

AbstractWe investigated attitudes toward 10 specific groups of individuals with disabilities among students in college settings. These groups comprised major depression, substance use disorder (SUD), anxiety disorder, autism spectrum disorder (ASD), cerebral palsy, hearing impairment, learning disability, visual impairment, spinal cord injury, and cancer survivor. The multidimensional scaling (MDS) analysis revealed a two-dimension space representing participants’ attitudes toward those disabilities. The MDS biplot further indicated higher levels of perceived dangerousness from the groups with SUD, major depression, anxiety disorder, and ASD. The hierarchical cluster analysis revealed that cluster A (SUD and major depression) was rated as having the highest level of social distance (i.e., negative attitudes). The implications for research and practice in rehabilitation counseling were discussed.

scholarly journals Conceptualizing researchers’ perspectives on involving professionals in research: a group concept mapping study

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Christine E. Laustsen ◽  
Albert Westergren ◽  
Pia Petersson ◽  
Maria Haak
Keyword(s):  
Data Collection ◽  
Concept Mapping ◽  
Hierarchical Cluster ◽  
Research Process ◽  
Scaling Analysis ◽  
Research Projects ◽  
Research And Practice ◽  
Group Concept Mapping ◽  
Process Adaptation ◽  
Group Concept

Abstract Background Researchers have shown an increased interest in involving professionals from outside academia in research projects. Professionals are often involved in research on ageing and health when the purpose is to address the gap between research and practice. However, there is a need to acquire more knowledge about what the involvement might lead to by exploring researchers’ experiences of involving professionals in research on ageing and health and developing conceptual areas. Therefore, the aim of this study was to identify conceptual areas of professionals’ involvement in research on ageing and health, from the perspective of researchers themselves. Methods Group concept mapping, a participatory and mixed method, was used to conceptualize areas. Researchers with experience of involving professionals in research projects on ageing and health participated in qualitative data collection through brainstorming sessions (n = 26), and by sorting statements (n = 27). They then took part in quantitative data collection, where they rated statements according to how much a statement strengthened research (n = 26) and strengthened practice (n = 24). Data were analysed using multidimensional scaling analysis and hierarchical cluster analysis. In addition, a qualitative analysis of the latent meaning of the cluster map was conducted. Results Analysis of the sorting stage generated five clusters illustrating conceptual areas of professionals’ involvement in research projects on ageing and health. The five clusters are as follows: complex collaboration throughout the research process; adaptation of research to different stakeholders, mutual learning through partnership; applicable and sustainable knowledge; legitimate research on ageing and health. The qualitative latent meaning of the cluster map showed two themes: the process of involvement and the outcome of involvement. A positive strong correlation (0.87) was found between the rating of strengthened research and practice. Conclusions This study reveals conceptual areas on a comprehensive and illustrative map which contributes to the understanding of professionals’ involvement in research on ageing and health. A conceptual basis for further studies is offered, where the aim is to investigate the processes and outcomes entailed in involving professionals in research on ageing and health. The study also contributes to the development of instruments and theories for optimizing the involvement of professionals in research.

scholarly journals Prediction of response to drug therapy in psychiatric disorders

Open Biology ◽  
2018 ◽  
Vol 8 (5) ◽  
pp. 180031 ◽  
Author(s):  
Shani Stern ◽  
Sara Linker ◽  
Krishna C. Vadodaria ◽  
Maria C. Marchetto ◽  
Fred H. Gage
Keyword(s):  
Autism Spectrum Disorder ◽  
Bipolar Disorder ◽  
Major Depression ◽  
Personalized Medicine ◽  
Psychiatric Disorders ◽  
Association Studies ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Genome Wide Association Studies ◽  
Patient Reported

Personalized medicine has become increasingly relevant to many medical fields, promising more efficient drug therapies and earlier intervention. The development of personalized medicine is coupled with the identification of biomarkers and classification algorithms that help predict the responses of different patients to different drugs. In the last 10 years, the Food and Drug Administration (FDA) has approved several genetically pre-screened drugs labelled as pharmacogenomics in the fields of oncology, pulmonary medicine, gastroenterology, haematology, neurology, rheumatology and even psychiatry. Clinicians have long cautioned that what may appear to be similar patient-reported symptoms may actually arise from different biological causes. With growing populations being diagnosed with different psychiatric conditions, it is critical for scientists and clinicians to develop precision medication tailored to individual conditions. Genome-wide association studies have highlighted the complicated nature of psychiatric disorders such as schizophrenia, bipolar disorder, major depression and autism spectrum disorder. Following these studies, association studies are needed to look for genomic markers of responsiveness to available drugs of individual patients within the population of a specific disorder. In addition to GWAS, the advent of new technologies such as brain imaging, cell reprogramming, sequencing and gene editing has given us the opportunity to look for more biomarkers that characterize a therapeutic response to a drug and to use all these biomarkers for determining treatment options. In this review, we discuss studies that were performed to find biomarkers of responsiveness to different available drugs for four brain disorders: bipolar disorder, schizophrenia, major depression and autism spectrum disorder. We provide recommendations for using an integrated method that will use available techniques for a better prediction of the most suitable drug.

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