• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

Comorbidities and emotions - unpacking the sentiments of pediatric patients with multiple long-term conditions through social media feedback: A large language model-driven study.

作者信息

Oluwalade Temidayo I, Ahmadi Hossein, Huo Lin, Sharpe Richard, Zhou Shang-Ming

机构信息

School of Engineering, Computing, and Mathematics, University of Plymouth, Plymouth PL4 8AA, United Kingdom.

Centre for Health Technology, Faculty of Health, University of Plymouth, Plymouth PL4 8AA, United Kingdom.

出版信息

J Affect Disord. 2025 Nov 1;388:119752. doi: 10.1016/j.jad.2025.119752. Epub 2025 Jun 21.

DOI:10.1016/j.jad.2025.119752
PMID:40550274
Abstract

OBJECTIVES

The emotional and psychological challenges faced by children with multiple long-term conditions (MLTCs) remain underexplored. This study aimed to analyze sentiments and emotions expressed by this vulnerable population and their caregivers on social media, assess the effects of comorbidities and the COVID-19 pandemic on emotional well-being.

METHODS

Narratives from the Care Opinion platform (2008-2023) were analyzed by a model called CoEmoBERT, developed using the large language model, distilroberta-base transformer model. The CoEmoBERT-based sentiment analysis categorized emotions into "Positive", "Negative", and "Neutral," with further refinements into specific emotions such as "Sad," "Fear", "Satisfied" etc. through pretraining and transferring process. Comorbidity associations with emotions were analyzed. We further examined the impact of the COVID-19 pandemic on patient sentiments and investigated temporal trends in emotional expressions.

RESULTS

Of 389 narratives, 93.8 % reflected negative sentiments, with "Sad" (60.9 %) and "Fear" (15.4 %) being the most prevalent. Negative emotions were linked to severe comorbidities like asthma, cancer, and chronic pain, highlighting the emotional burden of managing MLTCs. Positive sentiments (5.9 %) were associated with effective communication and exceptional healthcare experiences. The analysis revealed strong associations between certain comorbidity combinations and specific emotional responses, with mental health conditions showing the most diverse range of comorbidities and emotional impacts. The COVID-19 pandemic exacerbated negative sentiments, particularly sadness and disgust.

CONCLUSION

This study underscores the significant emotional burden on children with MLTCs, emphasizing the need for integrated care approaches to both physical and emotional well-being. These findings can guide the development of patient-centered care for this population.

摘要

相似文献

1
Comorbidities and emotions - unpacking the sentiments of pediatric patients with multiple long-term conditions through social media feedback: A large language model-driven study.
J Affect Disord. 2025 Nov 1;388:119752. doi: 10.1016/j.jad.2025.119752. Epub 2025 Jun 21.
2
Public Perception of the Brain-Computer Interface Based on a Decade of Data on X: Mixed Methods Study.基于X平台十年数据的公众对脑机接口的认知:混合方法研究
JMIR Form Res. 2025 Jun 25;9:e60859. doi: 10.2196/60859.
3
Public Health Messaging on Twitter During the COVID-19 Pandemic: Observational Study.新冠疫情期间推特上的公共卫生信息:观察性研究
J Med Internet Res. 2025 Feb 5;27:e63910. doi: 10.2196/63910.
4
Most Patients With Bone Sarcomas Seek Emotional Support and Information About Other Patients' Experiences: A Thematic Analysis.大多数骨肉瘤患者寻求情感支持和其他患者经验的信息:主题分析。
Clin Orthop Relat Res. 2024 Jan 1;482(1):161-171. doi: 10.1097/CORR.0000000000002761. Epub 2023 Jul 11.
5
Sentiment Analysis Using a Large Language Model-Based Approach to Detect Opioids Mixed With Other Substances Via Social Media: Method Development and Validation.使用基于大语言模型的方法通过社交媒体检测与其他物质混合的阿片类药物的情感分析:方法开发与验证
JMIR Infodemiology. 2025 Jun 19;5:e70525. doi: 10.2196/70525.
6
Exploring Social Media Posts on Lifestyle Behaviors: Sentiment and Content Analysis.探索社交媒体上关于生活方式行为的帖子:情感与内容分析
JMIR Infodemiology. 2025 Jun 25;5:e65835. doi: 10.2196/65835.
7
Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19.在基层医疗机构或医院门诊环境中,如果患者出现以下症状和体征,可判断其是否患有 COVID-19。
Cochrane Database Syst Rev. 2022 May 20;5(5):CD013665. doi: 10.1002/14651858.CD013665.pub3.
8
Public Perception of the Brain-Computer Interface: Insights from a Decade of Data on X.公众对脑机接口的认知:来自关于X的十年数据的见解。
JMIR Form Res. 2025 Jan 15. doi: 10.2196/60859.
9
A model of occupational stress to assess impact of COVID-19 on critical care and redeployed nurses: a mixed-methods study.一种评估 COVID-19 对重症护理和重新调配护士影响的职业压力模型:一项混合方法研究。
Health Soc Care Deliv Res. 2024 Dec 18:1-32. doi: 10.3310/PWRT8714.
10
Accreditation through the eyes of nurse managers: an infinite staircase or a phenomenon that evaporates like water.护士长眼中的认证:是无尽的阶梯还是如流水般消逝的现象。
J Health Organ Manag. 2025 Jun 30. doi: 10.1108/JHOM-01-2025-0029.