{"title":"为有特殊需求的乳腺癌患者提供护理支持:来自在线社区成员回复的证据。","authors":"Anqi Xu , Yuanyuan Gao","doi":"10.1016/j.ijmedinf.2024.105695","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Breast cancer is the most common cancer diagnosed in women globally. Online cancer communities (OCCs) provide platforms for breast cancer patients to connect, share experiences, and support each other. These communities facilitate discussions on a range of health- and non-health-related topics. However, posts discussing unique topics may receive varying levels of attention and support. This study aims to devise a method for identifying and supporting such posts, enhancing community response and support strategies.</div></div><div><h3>Methods</h3><div>We propose a Uniqueness Score Extraction Framework to compute health- and non-health-related uniqueness scores for online community posts. The framework utilizes deep learning-based natural language processing models to identify the topics discussed in OCCs and calculates the health- and non-health-related uniqueness scores of a post based on the uniqueness of the topics identified by the BERTopic model. We further employ econometric models to assess how the uniqueness scores of posts affect community members’ responses to those posts.</div></div><div><h3>Results</h3><div>Our study reveals that posts with a higher concentration of unique health-related topics in OCCs elicit quicker, more frequent, but shorter responses. Conversely, posts containing more unique non-health-related topics in the entire post prompt faster and longer responses, unless these topics become overly dominant, in which case the number of replies decreases, and response times are prolonged.</div></div><div><h3>Conclusion</h3><div>Our research develops a framework to identify posts with high uniqueness scores in OCCs, and sheds light on community member responses to these discussions. The findings indicate that while members are supportive, particularly regarding health-related topics, the post-content’s nature and focus greatly affect their engagement. These discoveries could enhance our understanding of community dynamics in OCCs, offering valuable implications for researchers, OCC facilitators, and medical professionals in supporting patients within online platforms.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"193 ","pages":"Article 105695"},"PeriodicalIF":3.7000,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Supporting the care to breast cancer patients with unique needs: Evidence from online community members’ responses\",\"authors\":\"Anqi Xu , Yuanyuan Gao\",\"doi\":\"10.1016/j.ijmedinf.2024.105695\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Breast cancer is the most common cancer diagnosed in women globally. Online cancer communities (OCCs) provide platforms for breast cancer patients to connect, share experiences, and support each other. These communities facilitate discussions on a range of health- and non-health-related topics. However, posts discussing unique topics may receive varying levels of attention and support. This study aims to devise a method for identifying and supporting such posts, enhancing community response and support strategies.</div></div><div><h3>Methods</h3><div>We propose a Uniqueness Score Extraction Framework to compute health- and non-health-related uniqueness scores for online community posts. The framework utilizes deep learning-based natural language processing models to identify the topics discussed in OCCs and calculates the health- and non-health-related uniqueness scores of a post based on the uniqueness of the topics identified by the BERTopic model. We further employ econometric models to assess how the uniqueness scores of posts affect community members’ responses to those posts.</div></div><div><h3>Results</h3><div>Our study reveals that posts with a higher concentration of unique health-related topics in OCCs elicit quicker, more frequent, but shorter responses. Conversely, posts containing more unique non-health-related topics in the entire post prompt faster and longer responses, unless these topics become overly dominant, in which case the number of replies decreases, and response times are prolonged.</div></div><div><h3>Conclusion</h3><div>Our research develops a framework to identify posts with high uniqueness scores in OCCs, and sheds light on community member responses to these discussions. The findings indicate that while members are supportive, particularly regarding health-related topics, the post-content’s nature and focus greatly affect their engagement. These discoveries could enhance our understanding of community dynamics in OCCs, offering valuable implications for researchers, OCC facilitators, and medical professionals in supporting patients within online platforms.</div></div>\",\"PeriodicalId\":54950,\"journal\":{\"name\":\"International Journal of Medical Informatics\",\"volume\":\"193 \",\"pages\":\"Article 105695\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-11-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Medical Informatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1386505624003587\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Medical Informatics","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1386505624003587","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Supporting the care to breast cancer patients with unique needs: Evidence from online community members’ responses
Background
Breast cancer is the most common cancer diagnosed in women globally. Online cancer communities (OCCs) provide platforms for breast cancer patients to connect, share experiences, and support each other. These communities facilitate discussions on a range of health- and non-health-related topics. However, posts discussing unique topics may receive varying levels of attention and support. This study aims to devise a method for identifying and supporting such posts, enhancing community response and support strategies.
Methods
We propose a Uniqueness Score Extraction Framework to compute health- and non-health-related uniqueness scores for online community posts. The framework utilizes deep learning-based natural language processing models to identify the topics discussed in OCCs and calculates the health- and non-health-related uniqueness scores of a post based on the uniqueness of the topics identified by the BERTopic model. We further employ econometric models to assess how the uniqueness scores of posts affect community members’ responses to those posts.
Results
Our study reveals that posts with a higher concentration of unique health-related topics in OCCs elicit quicker, more frequent, but shorter responses. Conversely, posts containing more unique non-health-related topics in the entire post prompt faster and longer responses, unless these topics become overly dominant, in which case the number of replies decreases, and response times are prolonged.
Conclusion
Our research develops a framework to identify posts with high uniqueness scores in OCCs, and sheds light on community member responses to these discussions. The findings indicate that while members are supportive, particularly regarding health-related topics, the post-content’s nature and focus greatly affect their engagement. These discoveries could enhance our understanding of community dynamics in OCCs, offering valuable implications for researchers, OCC facilitators, and medical professionals in supporting patients within online platforms.
期刊介绍:
International Journal of Medical Informatics provides an international medium for dissemination of original results and interpretative reviews concerning the field of medical informatics. The Journal emphasizes the evaluation of systems in healthcare settings.
The scope of journal covers:
Information systems, including national or international registration systems, hospital information systems, departmental and/or physician''s office systems, document handling systems, electronic medical record systems, standardization, systems integration etc.;
Computer-aided medical decision support systems using heuristic, algorithmic and/or statistical methods as exemplified in decision theory, protocol development, artificial intelligence, etc.
Educational computer based programs pertaining to medical informatics or medicine in general;
Organizational, economic, social, clinical impact, ethical and cost-benefit aspects of IT applications in health care.