Couples who are at risk of transmitting a genetic disease to their offspring may face difficult challenges regarding reproductive decision-making. Deciding if, and how, to purse their child wish can be a demanding process. This study aims to describe the reproductive joint decision-making process of genetically at-risk couples. A qualitative study was conducted with 16 couples (N=31) at risk of transmitting a genetic disease to their offspring and who received genetic counseling. Most couples were not aware of all available reproductive options in the Netherlands. A variety of motives was reported with almost all couples expressing a preference towards a reproductive option in which the child is genetically related to both parents. Only a few couples considered other options such as the use of donor gametes, adoption, and foster parenting. All couples indicated that they had multiple conversations to reach a mutually supported reproductive decision. Several carriers reported feelings of guilt and in some couples, the woman appeared to have a greater impact in the decision-making process as she should carry a pregnancy and should undergo medical treatments. This study provides insight in the extensive decision-making process of genetically at-risk couples and the role of both partners in this process. These findings can guide the development of genetic counseling (e.g., increase awareness of available reproductive options) and decision support for these couples.
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http://dx.doi.org/10.1007/s12687-021-00510-x | DOI Listing |
Anim Front
December 2024
Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI 53703, USA.
Explor Target Antitumor Ther
November 2024
Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy.
Current neural network models of primate vision focus on replicating overall levels of behavioral accuracy, often neglecting perceptual decisions' rich, dynamic nature. Here, we introduce a novel computational framework to model the dynamics of human behavioral choices by learning to align the temporal dynamics of a recurrent neural network (RNN) to human reaction times (RTs). We describe an approximation that allows us to constrain the number of time steps an RNN takes to solve a task with human RTs.
View Article and Find Full Text PDFCureus
December 2024
Centre for Population Research, Institute of Economic Growth, Delhi University, New Delhi, IND.
Introduction: Anemia is a severe public health problem in India, affecting more than 50% of individuals across most age groups. The Anemia Mukt Bharat (AMB) program, with a target of a three-percentage point reduction in anemia prevalence per year, developed a monitoring mechanism based on a set of 18 indicators and six key performance indicators (KPIs) derived from routine reporting in the Health Management Information System (HMIS). The study's objective was to assess the status of anemia control measures in the district of Faridabad, Haryana, India, using AMB HMIS indicators from April 2018 to March 2019.
View Article and Find Full Text PDFJAMIA Open
February 2025
Artificial Intelligence (AI) for Health Institute (AIHealth), Washington University in St Louis, St Louis, MO 63130, United States.
Objective: Extracorporeal membrane oxygenation (ECMO) is among the most resource-intensive therapies in critical care. The COVID-19 pandemic highlighted the lack of ECMO resource allocation tools. We aimed to develop a continuous ECMO risk prediction model to enhance patient triage and resource allocation.
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