A Scoping Review of Communication on Social Media - Breast Cancer

2021 ◽  
Vol 10 (1) ◽  
pp. 28-32
Author(s):  
Nikhil Shetty ◽  
Ye Yang ◽  
Onur Asan
Keyword(s):  
Breast Cancer ◽  
Social Media ◽  
Cancer Patients ◽  
Scoping Review ◽  
Healthcare Sector ◽  
Future Research ◽  
Breast Cancer Patients ◽  
Raising Awareness ◽  
Health Related ◽  
Use Of Social Media

Communication on social media enables people to express their views freely and makes them a part of the larger community. Social media is a form of communication that has been adopted in the healthcare sector gradually. Similarly, breast cancer patients also use social media to gain information and support from their providers and fellow cancer survivors. This study presents a scoping review of qualitative and quantitative studies to show different communication themes. The scoping review identified 38 eligible articles. The review identified three themes from the selected articles: raising awareness, social support, and reliability. These themes show the general trends and concerns among breast cancer patients and the use of social media. Future research needs to address these themes to enhance the online patient experience and use social media for health-related activities.

scholarly journals Annotating and Detecting Topics in Social Media Forum and Modelling the Annotation to Derive Directions-A Case Study

2021 ◽  
Author(s):  
Athira B ◽  
Josette Jones ◽  
Sumam Mary Idicula ◽  
Anand Kulanthaivel ◽  
Enming Zhang
Keyword(s):  
Breast Cancer ◽  
Social Media ◽  
Deep Learning ◽  
Cancer Patients ◽  
Learning Algorithm ◽  
Treatment Options ◽  
Healthcare Sector ◽  
Final Decision ◽  
Breast Cancer Patients ◽  
Deep Learning Algorithm

Abstract The widespread influence of social media impacts every aspect of life, including the healthcare sector. Although medics and health professionals are the final decision makers, the advice and recommendations obtained from fellow patients are significant. In this context, the present paper explores the topics of discussion posted by breast cancer patients and survivors on online forums. The study examines an online forum, Breastcancer.org, maps the discussion entries to several topics, and proposes a machine learning model based on a classification algorithm to characterize the topics. To explore the topics of breast cancer patients and survivors, approximately 1000 posts are selected and manually labeled with annotations. In contrast, millions of posts are available to build the labels. A semi-supervised learning technique is used to build the labels for the unlabeled data; hence, the large data are classified using a deep learning algorithm. The deep learning algorithm BiLSTM with BERT word embedding technique provided a better f1-score of 79.5%. This method is able to classify the following topics: medication reviews, clinician knowledge, various treatment options, seeking and providing support, diagnostic procedures, financial issues and implications for everyday life. What matters the most for the patients is coping with everyday living as well as seeking and providing emotional and informational support. The approach and findings show the potential of studying social media to provide insight into patients' experiences with cancer like critical health problems.

scholarly journals Annotating and detecting topics in social media forum and modelling the annotation to derive directions-a case study

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
B. Athira ◽  
Josette Jones ◽  
Sumam Mary Idicula ◽  
Anand Kulanthaivel ◽  
Enming Zhang
Keyword(s):  
Breast Cancer ◽  
Social Media ◽  
Deep Learning ◽  
Cancer Patients ◽  
Learning Algorithm ◽  
Treatment Options ◽  
Healthcare Sector ◽  
Final Decision ◽  
Breast Cancer Patients ◽  
Deep Learning Algorithm

AbstractThe widespread influence of social media impacts every aspect of life, including the healthcare sector. Although medics and health professionals are the final decision makers, the advice and recommendations obtained from fellow patients are significant. In this context, the present paper explores the topics of discussion posted by breast cancer patients and survivors on online forums. The study examines an online forum, Breastcancer.org, maps the discussion entries to several topics, and proposes a machine learning model based on a classification algorithm to characterize the topics. To explore the topics of breast cancer patients and survivors, approximately 1000 posts are selected and manually labeled with annotations. In contrast, millions of posts are available to build the labels. A semi-supervised learning technique is used to build the labels for the unlabeled data; hence, the large data are classified using a deep learning algorithm. The deep learning algorithm BiLSTM with BERT word embedding technique provided a better f1-score of 79.5%. This method is able to classify the following topics: medication reviews, clinician knowledge, various treatment options, seeking and providing support, diagnostic procedures, financial issues and implications for everyday life. What matters the most for the patients is coping with everyday living as well as seeking and providing emotional and informational support. The approach and findings show the potential of studying social media to provide insight into patients' experiences with cancer like critical health problems.

scholarly journals Annotating and Detecting Topics in Social Media Forum and Modelling the Annotation to Derive Directions-A Case Study

2020 ◽  
Author(s):  
Athira B ◽  
Josette Jones ◽  
Sumam Mary Idicula ◽  
Anand Kulanthaivel ◽  
Enming Zhang
Keyword(s):  
Breast Cancer ◽  
Social Media ◽  
Deep Learning ◽  
Cancer Patients ◽  
Learning Algorithm ◽  
Treatment Options ◽  
Healthcare Sector ◽  
Final Decision ◽  
Breast Cancer Patients ◽  
Deep Learning Algorithm

Abstract The widespread influence of social media impacts every aspect of life, including the healthcare sector. Although medics and health professionals are the final decision makers, the advice and recommendations obtained from fellow patients are significant. In this context, the present paper explores the topics of discussion posted by breast cancer patients and survivors on online forums. The study examines an online forum, Breastcancer.org, maps the discussion entries to several topics, and proposes a machine learning model based on a classification algorithm to characterize the topics. To explore the topics of breast cancer patients and survivors, approximately 1000 posts are selected and manually labeled with annotations. In contrast, millions of posts are available to build the labels. A semi-supervised learning technique is used to build the labels for the unlabeled data; hence, the large data are classified using a deep learning algorithm. The deep learning algorithm BiLSTM with BERT word embedding technique provided a better f1-score of 79.5%. This method is able to classify the following topics: medication reviews, clinician knowledge, various treatment options, seeking and providing support, diagnostic procedures, financial issues and implications for everyday life. What matters the most for the patients is coping with everyday living as well as seeking and providing emotional and informational support. The approach and findings show the potential of studying social media to provide insight into patients' experiences with cancer like critical health problems.

Adopting a knowledge translation approach in healthcare co-production. A case study

2020 ◽  
Vol 58 (9) ◽  
pp. 1841-1862 ◽  
Author(s):  
Francesca Dal Mas ◽  
Helena Biancuzzi ◽  
Maurizio Massaro ◽  
Luca Miceli
Keyword(s):  
Breast Cancer ◽  
Knowledge Translation ◽  
Cancer Patients ◽  
Healthcare Sector ◽  
Emotional Engagement ◽  
Breast Cancer Patients ◽  
Case Study Methodology ◽  
Content Type ◽  
Production Program

PurposeThe paper aims to contribute to the debate concerning the use of knowledge translation for implementing co-production processes in the healthcare sector. The study investigates a case study, in which design was used to trigger knowledge translation and foster co-production.Design/methodology/approachThe paper employs a case study methodology by analysing the experience of “Oncology in Motion”, a co-production program devoted to the recovery of breast cancer patients carried on by the IRCCS C.R.O. of Aviano, Italy.FindingsResults show how design could help to translate knowledge from various stakeholders with different skills (e.g. scientists, physicians, nurses) and emotional engagement (e.g. patients and patients' associations) during all the phases of a co-production project to support breast cancer patients in a recovery path. Stewardship theory is used to show that oncology represents a specific research context.Practical implicationsThe paper highlights the vast practical contribution that design can have in empowering knowledge translation at different levels and in a variety of co-production phases, among different stakeholders, facilitating their engagement and the achievement of the desired outcomes.Originality/valueThe paper contributes to the literature on knowledge translation in co-production projects in the healthcare sector showing how design can be effectively implemented.

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