This file provides bilingual Chinese-English transcripts of nine focus group discussions (FGDs) carried out in three Chinese cities in June and July 2012. The focus groups were commissioned by the authors from the Research Center for Contemporary China (RCCC) at Peking University as part of the ESRC project ‘Performance evaluations, trust and utilization of health care in China: understanding relationships between attitudes and health-related behaviour’. Local residents over the age of 30 took part in the discussions, which were moderated by a senior researcher from RCCC. The FGDs dealt with five main issues: how people know about changes in the health care system changes; how people make decisions to see a doctor when they are unwell; health care system evaluations; trust in doctors and the health care system; and what kind of a system people would like. The FGDs use a series of fictional scenarios (vignettes) to elicit responses concerning what influences people’s decisions about going to a doctor when they are unwell.This interdisciplinary project establishes a new collaboration among UK researchers and a leading Chinese social research team, to conduct the first major study of Chinese people's attitudes towards their health care. The project's core theoretical contribution is to understanding the relationships between attitudes and health-related behaviours, focussing particularly on how people evaluate their health system, their trust in doctors and the health system, and their utilization of preventive and curative health services. Previous quantitative research on health in China has examined the influence on utilization of age and gender, incomes, insurance protection, distance to health service providers and perceived health care needs. Yet work done in other countries has shown that attitudes, including performance evaluations and trust, can impact on people's decisions about when and where to use health services. At the same time, qualitative studies in China have suggested that people are often critical of performance and that there is a crisis of trust in doctors and the health care system. Our project is the first systematic study of these attitudes and how they influence utilization. The three cities chosen for focus group discussions, Chifeng, Yueyang and Shaoxing, represented respectively a city below the national average, close to the average and above the average in terms of GDP per capita. Two stratifications were used to select participants (see Focus Group Participant Profiles for details): Stratification One: of the general population by location and individual circumstances. This stratification was used in Chifeng and Shaoxing; all participants were local residents. In Chifeng, two discussions was conducted in the city itself and one discussion in a rural area under the city’s jurisdiction. In Shaoxing, one discussion was conducted in the city itself and one in a rural village within the city’s jurisdiction. Stratification Two: of patients by individual circumstances. This stratification was used in Yueyang. The participants in each of the four focus groups were screened by asking whether they had had contact with the health care system during the last two weeks in connection with an injury or illness; and what type of medical insurance they possessed. The initial intention was to stratify patients according to whether they reported suffering acute or chronic conditions. However, the difficulty of recruiting participants prevented this. The stratification of patients was thus according to their type of insurance. Nearly all participants on the first day of discussions (#4 and #5) had medical insurance equivalent to Urban Employees Basic Medical Insurance, whilst participants on the second day of discussions (#6 and #7) did not have this level of insurance. Most of these were members of the Rural Cooperative Medical Scheme, which gives them only limited entitlements to reimbursement of medical expenses in Yueyang.

Abstract

Improve Autism Screening by creating predicting the likelihood of having this condition.

About this dataset

What is Autism

Autism, or autism spectrum disorder (ASD), refers to a broad range of conditions characterized by challenges with social skills, repetitive behaviors, speech and nonverbal communication.

Causes and Challenges

It is mostly influenced by a combination of genetic and environmental factors. Because autism is a spectrum disorder, each person with autism has a distinct set of strengths and challenges. The ways in which people with autism learn, think and problem-solve can range from highly skilled to severely challenged. Research has made clear that high quality early intervention can improve learning, communication and social skills, as well as underlying brain development. Yet the diagnostic process can take several years.

The Role of Machine Learning

This dataset is composed of survey results for more than 700 people who filled an app form. There are labels portraying whether the person received a diagnosis of autism, allowing machine learning models to predict the likelihood of having autism, therefore allowing healthcare professionals prioritize their resources.

How to use

• Predict the likelihood of a person having autism using survey and demographic variables.
• Explore Autism across Gender, Age, and other variables

Acknowledgements

If you this dataset in your research, please credit the authors.

Citations

• Tabtah, F. (2017). Autism Spectrum Disorder Screening: Machine Learning Adaptation and DSM-5 Fulfillment. Proceedings of the 1st International Conference on Medical and Health Informatics 2017, pp.1-6. Taichung City, Taiwan, ACM.
• Thabtah, F. (2017). Machine Learning in Autistic Spectrum Disorder Behavioural Research: A Review. To Appear in Informatics for Health and Social Care Journal. December, 2017

License

Public Domain

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