Background: Cancer has different explanatory theories that address its etiology and treatment. It is usually associated with pain and suffering. Recently, new technologies, knowledge, and therapies have been developed, which may have transformed the classic social representations of the disease. This study aimed to understand the social representations (SRs) of cancer in patients from Medellín, Colombia.
Methods: This study used a grounded theory in 16 patients with cancer. The information was collected between June 2020 and May 2021. Information was analyzed following the open, axial, and selective coding stages.
Results: SRs of cancer at the time of diagnosis evoke negative connotations. However, cancer is redefined as a positive event as the clinical course of the disease progresses, and patients interact with health professionals and respond to treatment. The resignification of the disease depends on the etiological models of the patients, which include genetic, socio-anthropological, psychosocial, and psychogenic factors. In line with the SRs of etiology, patients seek out treatments complementary to the biomedical ones that can be socio-anthropological and psychogenic.
Conclusion: In this group negative representations about cancer persist, this way of understanding the disease is determined by the convergence of cultural meanings and personal experiences. The causal representation is connected to the actions and willingness of the patients to face their diagnosis. In this sense, two categories stand out: the first expresses that cancer is the consequence of a body subjected to excessive productivity; the second subsumes a psychogenic predisposition caused by the context where the ideology of happiness appears to be a social norm. This double saturation in which an individual is immersed results in new burdens that are not visible to caregivers and healthcare workers.
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http://dx.doi.org/10.3389/fsoc.2023.1257776 | DOI Listing |
Sci Rep
January 2025
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong, China.
Knowledge-aware recommendation systems often face challenges owing to sparse supervision signals and redundant entity relations, which can diminish the advantages of utilizing knowledge graphs for enhancing recommendation performance. To tackle these challenges, we propose a novel recommendation model named Dual-Intent-View Contrastive Learning network (DIVCL), inspired by recent advancements in contrastive and intent learning. DIVCL employs a dual-view representation learning approach using Graph Neural Networks (GNNs), consisting of two distinct views: a local view based on the user-item interaction graph and a global view based on the user-item-entity knowledge graph.
View Article and Find Full Text PDFJ Exp Child Psychol
January 2025
Division of Social Science, The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China. Electronic address:
Early computational capacity sets the foundation for mathematical learning. Preschool children have been shown to perform both non-symbolic addition and subtraction problems. However, it is still unknown how different operations affect the representational precision of the non-symbolic arithmetic solutions.
View Article and Find Full Text PDFSoc Sci Humanit Open
June 2024
University of Washington, Bothell, USA.
The first seven months of the US COVID-19 pandemic saw a massive increase in COVID-19-related crowdfunding campaigns. Despite their popularity, these campaigns were rarely successful in reaching their monetary goals, with nearly 40% of them not receiving a single donation. Previous research has indicated that crowdfunding has increased inequities and disparities in wealth, and this study set out to examine the situation in Washington State, an area greatly divided socio-economically, culturally, and geographically.
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