Dataset Information

Supporting Remote Survey Data Analysis by Co-researchers with Learning Disabilities through Inclusive and Creative Practices and Data Science Approaches.

ABSTRACT: Through a process of robust co-design, we created a bespoke accessible survey platform to explore the role of co-researchers with learning disabilities (LDs) in research design and analysis. A team of co-researchers used this system to create an online survey to challenge public understanding of LDs [3]. Here, we describe and evaluate the process of remotely co-analyzing the survey data across 30 meetings in a research team consisting of academics and non-academics with diverse abilities amid new COVID-19 lockdown challenges. Based on survey data with >1,500 responses, we first co-analyzed demographics using graphs and art & design approaches. Next, co-researchers co-analyzed the output of machine learning-based structural topic modelling (STM) applied to open-ended text responses. We derived an efficient five-steps STM co-analysis process for creative, inclusive, and critical engagement of data by co-researchers. Co-researchers observed that by trying to understand and impact public opinion, their own perspectives also changed.

SUBMITTER: Chapko D

PROVIDER: S-EPMC7611679 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Supporting Remote Survey Data Analysis by Co-researchers with Learning Disabilities through Inclusive and Creative Practices and Data Science Approaches.

Chapko Dorota D Rothstein Pedro P Emeh Lizzie L Frumiento Pino P Kennedy Donald D Mcnicholas David D Orjiekwe Ifeoma I Overton Michaela M Snead Mark M Steward Robyn R Sutton Jenny J Bradshaw Melissa M Jeffreys Evie E Gallia Will W Ewans Sarah S Williams Mark M Grierson Mick M

DIS. Designing Interactive Systems (Conference) 20210628

Through a process of robust co-design, we created a bespoke accessible survey platform to explore the role of co-researchers with learning disabilities (LDs) in research design and analysis. A team of co-researchers used this system to create an online survey to challenge public understanding of LDs [3]. Here, we describe and evaluate the process of remotely co-analyzing the survey data across 30 meetings in a research team consisting of academics and non-academics with diverse abilities amid ne ...[more]

PMID: 34541585

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Project description:IntroductionRecent advancements in big data analytics and the formation of large-scale clinical data repositories provide a unique opportunity to determine the current state of pediatric hearing health care for children who have developmental disabilities. Before answering unresolved questions about diagnostic practice, it is paramount to determine a standard and reliable method for identifying children who have reduced hearing because clinical management is affected by hearing status. The purpose of this study was to compare 5 different methods for identifying cases of reduced hearing from pure-tone thresholds based on developmental disability status.MethodsUsing retrospective clinical data from 100,960 children (0-18 years), hearing status was determined for a total of 226,580 encounters from three clinical sites. 9% of the children had a diagnosis of intellectual disability, autism spectrum disorder, Down syndrome, or cerebral palsy.ResultsResults revealed that encounters from children who have developmental disabilities were more likely to have insufficient data to allow hearing status to be determined. Moreover, methods with higher data demands (i.e., number of thresholds and ear-specific thresholds) resulted in fewer classifiable encounters. The average child age when hearing status was classified for the first time was older for children who have developmental disabilities than for children in the comparison group. Allowing thresholds to build up over multiple test sessions did result in more children who have developmental disabilities being classified than for single-encounter methods, but a meaningful decrease in child age at the time of classification was not seen for this strategy. Compared to the comparison group, children who have developmental disabilities were more likely to have reduced hearing that was stable over time, yet their hearing status was determined at older ages.DiscussionResults provide key guidance to researchers for how to determine hearing status in children for big data applications using electronic health records. Furthermore, several assessment disparities are spotlighted for children who have developmental disabilities that warrant further investigation.

Project description:BackgroundBetter research data management (RDM) provides the means to analyze data in new ways, effectively build on another researcher's results, and reproduce the results of an experiment. Librarians are recognized by many as a potential resource for assisting researchers in this area, however this potential has not been fully realized in the biomedical research community. While librarians possess the broad skill set needed to support RDM, they often lack specific knowledge and time to develop an appropriate curriculum for their research community. The goal of this project was to develop and pilot educational modules for librarians to learn RDM and a curriculum for them to subsequently use to train their own research communities.Materials and methodsWe created online modules for librarians that address RDM best practices, resources and regulations, as well as the culture and practice of biomedical research. Data was collected from librarians through questions embedded in the online modules on their self-reported changes in understanding of and comfort level with RDM using a retrospective pre-post design. We also developed a Teaching Toolkit which consists of slides, a script, and an evaluation form for librarians to use to teach an introductory RDM class to researchers at their own institutions. Researchers' satisfaction with the class and intent to use the material they had learned was collected. Actual changes in RDM practices by researchers who attended was assessed with a follow-up survey administered seven months after the class.Results and discussionThe online curriculum increased librarians' self-reported understanding of and comfort level with RDM. The Teaching Toolkit, when employed by librarians to teach researchers in person, resulted in improved RDM practices. This two-tiered curriculum provides concise training and a ready-made curriculum that allows working librarians to quickly gain an understanding of RDM, and translate this knowledge to researchers through training at their own institutions.

Dataset Information

Supporting Remote Survey Data Analysis by Co-researchers with Learning Disabilities through Inclusive and Creative Practices and Data Science Approaches.

Publications

Supporting Remote Survey Data Analysis by Co-researchers with Learning Disabilities through Inclusive and Creative Practices and Data Science Approaches.

Similar Datasets

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