Many women diagnosed with breast cancer today can expect to live long after completing their treatment. This growing population of survivors encounters distinct post-treatment health and information needs. Existing survivorship care models take information as a given, black boxing it. I use Actor-Network Theory to examine how information actually works for women after they complete breast cancer treatment, and how it shapes their understanding of survivorship. I draw on in-depth interviews with breast cancer survivors (n = 82) and a wide range of providers (n = 84) in a medically underserved region of Southern California. Black boxes and information pathways convey experiential dimensions of cancer care; they are also metaphoric constructs. The black box metaphor refers to the cancer experience as a container; the pathways metaphor refers to a journey. Each of these metaphors expresses salient dimensions of the cancer experience and has implications for post-treatment survivorship. When healthcare information flows smoothly and invisibly, its pathways become black boxed. Black boxes can be helpful when they function effectively. But since black boxes conceal their inner workings, it is challenging to intervene when difficulties arise. I provide three examples of difficulties that complicate women's transition to post-treatment survivorship: (1) when survivors fail to recognize treatment-related late effects, (2) do not understand they have a terminal diagnosis, or (3) worry that their treatment accomplished nothing. Contextualized within survivorship scholarship, this study recommends opening black boxes to examine how information pathways could connect women differently to improve survivorship care.
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http://dx.doi.org/10.1016/j.socscimed.2022.115184 | DOI Listing |
Forensic Sci Int Genet
January 2025
Computer Science Department, University of Buenos Aires, Faculty of Exact and Natural Sciences, Buenos Aires, Argentina.
Forensic scientists play a crucial role in assigning probabilities to evidence based on competing hypotheses, which is fundamental in legal contexts where propositions are presented usually by prosecution and defense. The likelihood ratio (LR) is a well-established metric for quantifying the statistical weight of the evidence, facilitating the comparison of probabilities under these hypotheses. Developing accurate LR models is inherently complex, as it relies on cumulative scientific knowledge.
View Article and Find Full Text PDFJ Am Med Inform Assoc
January 2025
Department of Computer Science, Duke University, Durham, NC 27708, United States.
Objective: Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (that might lead to the loss in performance). We aim to bridge the gap between these 2 categories by building on modern interpretable machine learning (ML) techniques to design interpretable mortality risk scores that are as accurate as black boxes.
Material And Methods: We developed a new algorithm, GroupFasterRisk, which has several important benefits: it uses both hard and soft direct sparsity regularization, it incorporates group sparsity to allow more cohesive models, it allows for monotonicity constraint to include domain knowledge, and it produces many equally good models, which allows domain experts to choose among them.
Diagnostics (Basel)
January 2025
Aerospace Engineering Department and Interdisciplinary Research Center for Smart Mobility and Logistics, and Interdisciplinary Research Center Aviation and Space Exploration, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
Artificial intelligence (AI) has recently made unprecedented contributions in every walk of life, but it has not been able to work its way into diagnostic medicine and standard clinical practice yet. Although data scientists, researchers, and medical experts have been working in the direction of designing and developing computer aided diagnosis (CAD) tools to serve as assistants to doctors, their large-scale adoption and integration into the healthcare system still seems far-fetched. Diagnostic radiology is no exception.
View Article and Find Full Text PDFBackground: Previous studies on public compliance with policies during pandemics have primarily explained it from the perspectives of motivation theory, focusing on normative motivation (trust in policy-making institutions) and calculative motivation (fear of contracting the disease). However, the social amplification of a risk framework highlights that the media plays a key role in this process.
Objective: This study aims to integrate the motivation theory of compliance behavior and the social amplification of risk framework to uncover the "black boxes" of the mechanisms by which normative motivation and calculative motivation influence public policy compliance behavior through the use of media.
Sci Rep
January 2025
Research Innovation and Entrepreneurship Unit, University of Buraimi, 512, Buraimi, Oman.
Skin diseases impact millions of people around the world and pose a severe risk to public health. These diseases have a wide range of effects on the skin's structure, functionality, and appearance. Identifying and predicting skin diseases are laborious processes that require a complete physical examination, a review of the patient's medical history, and proper laboratory diagnostic testing.
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