Breast cancer is the most frequently diagnosed type of cancer among women and has the highest survival rate among all cancer. Breast cancer treatment has a negative effect on upper extremity function, which adversely impacts patients’ quality of life. Therefore, in order to fully recover, patients need to undergo rehabilitation therapy. Nevertheless, due to intense multimodality therapy, patients also suffer from cancer-related fatigue and diminished self-efficacy, which become the main obstacles to starting rehabilitation. In order to find a solution to this problem, scholars developed Rehabilitation Empowerment Model, which prioritizes the role of rehabilitation practitioner in enhancing self-efficacy through empowerment. Nevertheless, patients report being unaware of necessity of rehabilitation and existing opportunities, which should be communicated to them by healthcare practitioners. Since it turns out that breast cancer patients often seek support online before they undergo the treatment, this thesis investigates whether social media discussions can compliment the role of rehabilitation practitioner in enhancing self-efficacy through empowerment. In order to do it, the data in form of text corpus was fetched from four most popular Facebook pages devoted to breast cancer. Using supervised machine learning the text was labelled into number of constructs of domain-specific models for text classification, which were created based on relevant theories of self-efficacy and empowerment. The analysis was based on the results obtained from text classification. It turns out that both user comments and admin post do reflect constructs of Self-efficacy Development Model, Perceived Self-efficacy Model and Model of Psychological Empowerment. These and other insights serve as useful information for healthcare sector to explore the potential of using social media for informative purposes but also empowering patients to take control over post-treatment management of their illness.
|Educations||MSc in Business Administration and Information Systems, (Graduate Programme) Final Thesis|
|Number of pages||89|
|Supervisors||Raghava Rao Mukkamala|