Appendix 1 contains terms and concepts related to health and longevity. The research team has reviewed literature and sorted out domestic and foreign terms and concepts related to health and longevity, including healthy aging (6), active aging (7), successful aging (4), and healthy longevity (3), providing reference for exploring the definition of health and longevity. Appendix 2 is the first round of Delphi expert consultation questionnaire, which includes basic information of experts (age, gender, highest education level, professional title, workplace, work field, and years related to elderly health) and a list of consensus items on the definition of health and longevity (4 multiple-choice questions, 4 multiple-choice questions, and 1 open-ended question). Appendix 3 and Appendix 4 respectively provide detailed data on the questions, options, number of respondents, and consensus reached in the first and second rounds of the consultation questionnaire. In the first round, consensus was reached on 4 items, and in the second round, consensus was reached on 5 items, totaling 9 items.

BackgroundOral health literacy (OHL) has been recognized as a component of oral health disparities; however, the precise relationship between literacy and oral health outcomes has not been established. To explore the role of parents' OHL for their own subjective oral health, related behavior, and for the proxy assessment of their child's oral health, oral health-related behavior.MethodsSurvey data were collected from 406 parents of 4- to 7-year-old children in Beijing, China. The background characteristics, oral health assessment, oral health-related behavior, knowledge and attitudes, and diet-related questions of parents and their children were surveyed by a questionnaire. OHL was assessed with the Hong Kong Rapid Estimate of Adult Literacy in Dentistry (HKREAL-30) Scale and a revised version that asked the respondents to indicate if they understood the words (HKREALD-30-Understand).ResultsThe HKREALD-30 responses correlated with the HKREALD-30-Understand responses. The higher the parents' HKREALD-30-Understand scores, the better they described the health of their own teeth and gums, the greater their child's diet was influenced by the protein, sugar and calories of the food, and the more positive their oral health-related attitudes were. The higher the parent's HKREALD-30 scores, the healthier they described their child's teeth and gums.ConclusionsBoth the HKREALD-30 and HKREALD-30-Understand Scores correlate with parents' self and proxy oral health-related responses. Chinese parents could understand that the word would add predictive value to the prediction of how parents' oral health literacy affects their own oral health care, children's oral health and other related aspects.

Introduction China Family Panel Studies (CFPS) is a nationally representative, biennial longitudinal survey of Chinese communities, families, and individuals launched in 2010 by the Institute of Social Science Survey (ISSS) of Peking University, China. The CFPS is designed to collect individual-, family-, and community-level longitudinal data in contemporary China. The studies focus on the economic, as well as the non-economic, wellbeing of the Chinese population, with a wealth of information covering such topics as economic activities, education outcomes, family dynamics and relationships, migration, and health. The CFPS is funded by the Chinese government through Peking University. The CFPS promises to provide to the academic community the most comprehensive and highest-quality survey data on contemporary China.

The sample for the 2010 CFPS baseline survey through a multi-stage probability is drawn with implicit stratification. It is designed to be multi-stage so as both to reduce the operational cost of the survey and to allow for studies of social contexts. Each subsample in the CFPS study is drawn through three stages: county (or equivalent), then village (or equivalent), then household.

Interviews are conducted using computer assisted personal interviewing (CAPI) or computer assisted telephone interviewing (CATI) technology,. The computer assisted interviews enable the researchers to implement complex questionnaire designs tailored to each member of the household and reduces measurement error while at the same time allowing the management team at the ISSS to closely monitor the quality of the interviews in the field.

A large number of dedicated staff have made tremendous efforts and selfless contributions to the CFPS. The achievements of the CFPS are the result of collective wisdom and hard work, involving experts and scholars from various professional fields across domestic and international institutions, diligent personnel responsible for survey implementation, a reliable data team providing high-quality and user-friendly data, and field interviewers working tirelessly on the front lines.

The China Health and Nutrition Survey (CHNS) started in the year 1989. The survey had the goal of creating a multilevel method of data collection from both individual and Chinese households. This collecting method had the aim of understanding all sorts of economic and social changes affecting nutrition and health outcomes in China. Though the survey started in 1989, it covers up to the year 2015. In this survey in-depth study and collection of new household formation and community data, was made. CHNS includes data on a total of three autonomous (Beijing, Chongqing, and Shanghai) cities and twelve provinces (Jiangsu, Liaoning, Guangxi, Guizhou, Heilongjiang, Henan, Hubei, Hunan, Shaanxi, Shandong, Yunnan, and Zhejiang). The in-depth sampling measure of this survey allowed for the construction of multiple urbanicity measures. These measures were used and studied in multiple types of research either jointly or separately. The remaining of the data could be obtained on demand from its official website : https://www.cpc.unc.edu/projects/china

Please use the following acknowledgment in all publications resulting from use of the China Health and Nutrition Survey data:

This research uses data from China Health and Nutrition Survey (CHNS). We thank the National Institute for Nutrition and Health, China Center for Disease Control and Prevention, Carolina Population Center (P2C HD050924, T32 HD007168), the University of North Carolina at Chapel Hill, the NIH (R01-HD30880, DK056350, R24 HD050924, and R01-HD38700) and the NIH Fogarty International Center (D43 TW009077, D43 TW007709) for financial support for the CHNS data collection and analysis files from 1989 to 2015 and future surveys, and the China-Japan Friendship Hospital, Ministry of Health for support for CHNS 2009, Chinese National Human Genome Center at Shanghai since 2009, and Beijing Municipal Center for Disease Prevention and Control since 2011.

Seasonal concentration index and Herfindahl index of public attention index in China.

China is an emerging country, and government intervention is always considered as an important part of the solutions when people facing challenges in China. Under the impact of the coronavirus disease 2019 (COVID-19) epidemic and the global economic downturn, the Chinese government quickly brought the epidemic under control and restored the positive economic growth through strong intervention. Based on the panel data of provincial level in China and the government intervention as the threshold variable, this paper empirically analyzed the non-linear effect of business cycle on population health by using the panel threshold regression model. The empirical results show that the impact of the business cycle on population health is significantly negative, and government intervention has a single threshold effect on the relationship between business cycle and population health. When the government intervention is below the threshold value, the business cycle has a significant negative effect on the improvement of the population health level; when the level of government intervention exceeds the threshold value, the relationship between business cycle and population health becomes significantly positive. To some extent, the conclusions of this paper can guide the formulation and revision of government health policy and help to adjust the direction and intensity of government intervention. The Chinese government and other governments of emerging countries should do more to harness the power of state intervention in their response to the business cycle.

The CHERRY study aims to collect electronic health records (EHRs) of adults in Yinzhou, an economically advanced region in southeastern China, serving as a big data resource for cardiovascular risk prediction and population management and providing evidence to improve the primary and secondary prevention of cardiovascular events in China. The study population consists of permanent residents of Chinese nationality residing in Yinzho who were 18 years of age or older as of January 1, 2009. In 2020, the study population consisted of 1.25 million individuals. The study includes data since 2009, and participants are continuously followed up through record linkage.

Social pull-push factors mainly fall into six categories: food, traffic, education, technology, health and medical conditions and human living conditions. Indicators of total grain product (Million tons), number of health agencies (units), number of beds in health care agencies (1000 beds), length of railways (10000 km), length of highways (10000 km), length of navigable inland waterways (10000 km), number of regular primary schools (units), number of higher education institutions (units), number of patent applications (units), per capita annual income of urban households (yuan), per capita annual income of rural households (yuan), Engel's coefficient of urban households (-), Engel's coefficient of rural households(-).Time serial data from 1949 to 2013 of whole China and all the provinces are included. All of data were collected from the China Statistical Yearbook from 1981 to 2014 and China Compendium of Statistics from 1949 to 2008.These data are not intended for demarcation.

Rui-Ci Health Care DataData were extracted from a computerized database established by the Rich Healthcare Group in China, which includes all medical records for participants who received a health check from 2010 to 2016.RC Health Care Data-20180820.xlsx

Techsalerator's Job Openings Data for China: A Comprehensive Resource for Employment Insights

Techsalerator's Job Openings Data for China offers a detailed and essential resource for businesses, job seekers, and labor market analysts. This dataset provides an in-depth view of job openings across various industries in China, collating information from numerous sources such as company websites, job boards, and recruitment agencies.

Key Data Fields

• Job Posting Date: Captures the listing date for each job opening, keeping job seekers and HR professionals up-to-date with the latest opportunities and market trends.
• Job Title: Details the specific role being advertised, helping categorize and filter job openings by industry and career focus.
• Company Name: Lists the hiring organizations, enabling job seekers to focus their applications and helping businesses monitor competitors and industry trends.
• Job Location: Specifies the geographic location of the job within China, aiding job seekers in finding regional opportunities and assisting employers in evaluating regional labor markets.
• Job Description: Provides comprehensive information about the responsibilities, qualifications, and skills required, offering clarity to both candidates and recruiters.

Top 5 Job Categories in China

1. Information Technology (IT): A booming sector with high demand for software developers, data scientists, and cybersecurity experts due to the rapid growth of China's digital economy.
2. Manufacturing: Significant demand for engineers, production managers, and skilled laborers in one of the world’s largest manufacturing hubs.
3. Finance and Banking: High demand for financial analysts, investment managers, and compliance officers as China’s financial sector continues to expand.
4. Healthcare: Roles for doctors, nurses, and healthcare administrators driven by the increasing demand for healthcare services due to population growth and aging.
5. E-Commerce and Retail: Opportunities for logistics managers, supply chain analysts, and digital marketing specialists, reflecting China's leadership in the global e-commerce market.

Top 5 Employers in China

1. Alibaba Group: A leading e-commerce company with frequent openings in logistics, IT, marketing, and management roles.
2. Tencent: A technology giant offering positions in software development, gaming, and cloud computing.
3. China National Petroleum Corporation (CNPC): Major employer in the energy sector with roles in engineering, management, and technical services.
4. China Construction Bank: One of the largest banks in China, regularly hiring in areas like banking operations, financial analysis, and customer service.
5. Huawei Technologies: A global telecommunications company offering roles in R&D, engineering, sales, and project management.

Accessing Techsalerator’s Data

To access Techsalerator’s Job Openings Data for China, please contact info@techsalerator.com with your specific data requirements. We will provide a customized quote based on the data fields and records you need, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Job Posting Date
• Job Title
• Company Name
• Job Location
• Job Description
• Application Deadline
• Job Type (Full-time, Part-time, Contract)
• Salary Range
• Required Qualifications
• Contact Information

Techsalerator’s dataset serves as a valuable tool for tracking employment trends and job opportunities in China, empowering businesses, job seekers, and analysts to make informed decisions.

Supplemental files for the The China Health and Nutrition Survey (CHNS) including variable index. CHNS was designed to examine the effects of the health, nutrition, and family planning policies and programs implemented by national and local governments and to see how the social and economic transformation of Chinese society is affecting the health and nutritional status of its population. The impact on nutrition and health behaviors and outcomes is gauged by changes in community organizations and programs as well as by changes in sets of household and individual economic, demographic, and social factors. CHNS covers nine provinces that vary substantially in geography, economic development, public resources, and health indicators.

Recommended dietary allowance for nutrients for the people in the country

Public health-related decision-making on policies aimed at controlling the COVID-19 pandemic outbreak depends on complex epidemiological models that are compelled to be robust and use all relevant available data. This data article provides a new combined worldwide COVID-19 dataset obtained from official data sources with improved systematic measurement errors and a dedicated dashboard for online data visualization and summary. The dataset adds new measures and attributes to the normal attributes of official data sources, such as daily mortality, and fatality rates. We used comparative statistical analysis to evaluate the measurement errors of COVID-19 official data collections from the Chinese Center for Disease Control and Prevention (Chinese CDC), World Health Organization (WHO) and European Centre for Disease Prevention and Control (ECDC). The data is collected by using text mining techniques and reviewing pdf reports, metadata, and reference data. The combined dataset includes complete spatial data such as countries area, international number of countries, Alpha-2 code, Alpha-3 code, latitude, longitude, and some additional attributes such as population. The improved dataset benefits from major corrections on the referenced data sets and official reports such as adjustments in the reporting dates, which suffered from a one to two days lag, removing negative values, detecting unreasonable changes in historical data in new reports and corrections on systematic measurement errors, which have been increasing as the pandemic outbreak spreads and more countries contribute data for the official repositories. Additionally, the root mean square error of attributes in the paired comparison of datasets was used to identify the main data problems. The data for China is presented separately and in more detail, and it has been extracted from the attached reports available on the main page of the CCDC website. This dataset is a comprehensive and reliable source of worldwide COVID-19 data that can be used in epidemiological models assessing the magnitude and timeline for confirmed cases, long-term predictions of deaths or hospital utilization, the effects of quarantine, stay-at-home orders and other social distancing measures, the pandemic’s turning point or in economic and social impact analysis, helping to inform national and local authorities on how to implement an adaptive response approach to re-opening the economy, re-open schools, alleviate business and social distancing restrictions, design economic programs or allow sports events to resume.

Age-adjusted odds ratios (OR) for hospitalization and ICU admission of infected individuals with underlying health conditions and the prevalence of underlying health conditions in the Chinese population.

Introduction

This Mandarin Chinese Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Mandarin speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native Mandarin Chinese speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

Chinese Longitudinal Healthy Longevity Survey (CLHLS) WELCOME! The Chinese Longitudinal Healthy Longevity Survey (CLHLS) has been supported by NIA/NIH grants R01 AG023627-01 (PI: Zeng Yi) (Grant name: Demographic Analysis of Healthy Longevity in China) and P01 AG 008761 (PI: Zeng Yi; Program Project Director: James W. Vaupel), awarded to Duke University, with Chinese matching support for personnel costs and some local expenses. UNFPA and the China Social Sciences Foundation provided additional support for expanding the 2002 CLHLS survey. The Max Planck Institute for Demographic Research has provided support for international training since the CLHLS 1998 baseline survey. Finally, in December 2004 the China Natural Sciences Foundation and the Hong Kong Research Grants Council (RGC) partnered with NIA/NIH, providing grants to partially support the CLHLS project. Until present, the CLHLS conducted face-to-face interviews with 8,959, 11,161, 20,421, 18,524 and 19,863 individuals in 1998, 2000, 20002, 2005, and 2008-09, respectively, using internationally compatible questionnaires. Among the approximately 80,000 interviews conducted in the five waves, 14,290 were with centenarians, 18,910 with nonagenarians, 20,743 with octogenarians, 14,416 with younger elders aged 65-79, and 10,569 with middle-age adults aged 35-64. At each wave, survivors were re-interviewed, and deceased interviewees were replaced with new participants. Data on mortality and health status before dying for the 17,721 elders aged 65-110 who died between waves were collected in interviews with a close family member of the deceased. The CLHLS has the largest sample of centenarians in the world according to a report in Science (see the report). Our general goal is to shed new light on a better understanding of the determinants of healthy longevity of human beings. We are compiling extensive data on a much larger population of the oldest-old aged 80-112 than has previously been studied, with a comparison group of younger elders aged 65-79. We propose to use innovative demographic and statistical methods to analyze longitudinal survey data. Our goal is to determine which factors, out of a large set of social, behavioral, biological, and environmental risk factors, play an important role in healthy longevity. The large population size, the focus on healthy longevity (rather than on a specific disease or disorder), the simultaneous consideration of various risk factors, and the use of analytical strategies based on demographic concepts make this an innovative demographic data collection and research project. Our specific objectives are as follows: Collect intensive individual interview data including health, disability, demographic, family, socioeconomic, and behavioral risk factors for mortality and healthy longevity. Follow up the oldest-old and the comparison group of the younger elders, as well as some of the elders’ adult children to ascertain changes in their health status, care needs and costs, and associated factors. We will also ascertain mortality and causes of death, as well as care needs, costs, and health/disability status before death. Analyze the collected data to estimate the impacts of social, behavioral, environmental, and biological risk factors that are determinants of healthy longevity and mortality in the oldest-old. Compare the findings with results from other studies of large populations at advanced age.

Chinese Medical Dialogue

Deep Learning for Intelligent Healthcare

By Huggingface Hub [source]

About this dataset

This dataset is designed to train a deep learning language model for intelligent healthcare using Chinese medical dialogue. It includes different components such as pretraining, finetuning and reward data which allows the model to learn how to produce more accurate answers in the medical context. The dataset consists of columns containing questions, chosen responses and rejected responses allowing it to view multiple perspectives when constructing a conversation. This makes the model not only more precise but also reinforces its ability to engage with medical dialogue at an advanced level, making it great resource for businesses, researchers or any individual looking into developing their own intelligent healthcare system

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can be used to train an intelligent language model for medical dialogue in Chinese. To use this dataset, one would need to get familiar with the following steps:

• Pretraining - Use the pretraining data provided in the dataset to build and fine-tune a language model. This will help the model understand basic elements of the medical dialogue and acquire general knowledge about Chinese medicine.

• Finetuning - Use the finetune data and apply transfer learning techniques such as distrust learning or multi-task learning to further improve model accuracy on specific tasks such as medical related questions and responses.

• Reward - Make use of rewards from patient or doctor for correct responses, which will help boost performance of AI systems by guiding them with real feedback from experienced healthcare professionals or patients themselves based on their understanding of medicine knowledge in long dialogue flows interviews or discussions .

• Evaluation - After training with pretraining/finetuning/reward datasets, make sure you evaluate your trained models on unseen data using reward_validation file which is provided along with the dataset itself to assess its performance level effectively

Research Ideas

• Utilizing reinforcement learning with the reward data for training a dialogue model that rewards correct responses.
• Employing few-shot learning methods to quickly adapt the pretraining data for new and unseen medical dialogues.
• Exploring transfer learning techniques to apply knowledge learned from one medical domain to another

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: reward_train.csv | Column name | Description | |:----------------------|:---------------------------------------------------------------------| | question | The question asked in the medical dialogue. (String) | | response_chosen | The response chosen by the model as the correct answer. (String) | | response_rejected | The response rejected by the model as the incorrect answer. (String) |

File: reward_test.csv | Column name | Description | |:----------------------|:---------------------------------------------------------------------| | question | The question asked in the medical dialogue. (String) | | response_chosen | The response chosen by the model as the correct answer. (String) | | response_rejected | The response rejected by the model as the incorrect answer. (String) |

File: reward_validation.csv | Column name | Description | |:----------------------|:---------------------------------------------------------------------| | question | The question asked in the medical dialogue. (String) | | response_chosen | The response chosen by the model as the correct answer. (String) | | response_rejected | The response rejected by the model as the incorrect answe...

The China Multi-Generational Panel Dataset - Liaoning (CMGPD-LN) is drawn from the population registers compiled by the Imperial Household Agency (neiwufu) in Shengjing, currently the northeast Chinese province of Liaoning, between 1749 and 1909. It provides 1.5 million triennial observations of more than 260,000 residents from 698 communities. The population mainly consists of immigrants from North China who settled in rural Liaoning during the early eighteenth century, and their descendants. The data provide socioeconomic, demographic, and other characteristics for individuals, households, and communities, and record demographic outcomes such as marriage, fertility, and mortality. The data also record specific disabilities for a subset of adult males. Additionally, the collection includes monthly and annual grain price data, custom records for the city of Yingkou, as well as information regarding natural disasters, such as floods, droughts, and earthquakes. This dataset is unique among publicly available population databases because of its time span, volume, detail, and completeness of recording, and because it provides longitudinal data not just on individuals, but on their households, descent groups, and communities.

BackgroundConsidering that the previously developed mortality prediction models have limited applications to the Chinese population, a questionnaire-based prediction model is of great importance for its accuracy and convenience in clinical practice.MethodsTwo national cohort, namely, the China Health and Nutrition Survey (8,355 individual older than 18) and the China Health and Retirement Longitudinal Study (12,711 individuals older than 45) were used for model development and validation. One hundred and fifty-nine variables were compiled to generate predictions. The Cox regression model and six machine learning (ML) models were used to predict all-cause mortality. Finally, a simple questionnaire-based ML prediction model was developed using the best algorithm and validated.ResultsIn the internal validation set, all the ML models performed better than the traditional Cox model in predicting 6-year mortality and the random survival forest (RSF) model performed best. The questionnaire-based ML model, which only included 20 variables, achieved a C-index of 0.86 (95%CI: 0.80–0.92). On external validation, the simple questionnaire-based model achieved a C-index of 0.82 (95%CI: 0.77–0.87), 0.77 (95%CI: 0.75–0.79), and 0.79 (95%CI: 0.77–0.81), respectively, in predicting 2-, 9-, and 11-year mortality.ConclusionsIn this prospective population-based study, a model based on the RSF analysis performed best among all models. Furthermore, there was no significant difference between the prediction performance of the questionnaire-based ML model, which only included 20 variables, and that of the model with all variables (including laboratory variables). The simple questionnaire-based ML prediction model, which needs to be further explored, is of great importance for its accuracy and suitability to the Chinese general population.

ObjectiveTo comprehensively determine the prevalence of MRSA in healthy Chinese population, the influencing factors of MRSA colonization and its antibiotic resistance.MethodsArticles that studied prevalence or influencing factors of MRSA carriage in healthy Chinese population were retrieved from PubMed, Ovid database, three Chinese electronic databases. The pooled prevalence of MRSA, its antibiotic resistance and influencing factors were analyzed by STATA12.0.Results37 studies were included. The pooled prevalence of MRSA was 21.2% (95% CI: 18.5%-23.9%), and the prevalence of S.aureus was 15% (95% CI: 10%-19%), with a significant heterogeneity (MRSA: I2 = 97.6%, P