Aim: Midwives are expected to identify and help resolve ethics problems that arise in practice, skills that are presumed to be taught in midwifery educational programs. In this study, we explore how midwives recognize ethical dilemmas in clinical practice and examine the sources of their ethics education. Methods: We conducted semi-structured, individual interviews with midwives from throughout the United States (U.S.) (n = 15). Transcripts of the interviews were analysed using an iterative process to identify themes and subthemes. Findings: Midwives described a range of professional ethical dilemmas, including challenges related to negotiating strained interprofessional relationships and protecting or promoting autonomy for women. Ethical dilemmas were identified by the theme of unease, a sense of distress that was expressed in three subthemes: uncertainty of action, compromise in action, and reflecting on action. Learning about ethics and ethical dilemmas occurred, for the most part, outside of the classroom, with the majority of participants reporting that their midwifery program did not confer the skills to identify and resolve ethical challenges. Conclusion: Midwives in this study reported a range of ethical challenges and minimal classroom education related to ethics. Midwifery educators should consider the purposeful and explicit inclusion of midwifery-specific ethics content in their curricula and in interprofessional ethics education. Reflection and self-awareness of bias were identified as key components of understanding ethical frameworks. As clinical preceptors were identified as a key source of ethics learning, midwifery educators should consider ways to support preceptors in building their skills as role models and ethics educators.
In this chapter, we propose an ethical framework for serious game design, which we term the Ecosystem for Designing Games Ethically (EDGE).EDGE expands on Zagal’s categorization of ethical areas in game design by incorporating the different contexts of design and their use. In addition, we leverage these contexts to suggest four guidelines that support Ethical Stewardship in serious game design. We conclude by discussing a number of specific areas inwhich ethics plays a role in serious game design. These include games in (a) amilitary context, (b) the consideration of privacy issues, and (c) the evaluation ofgame design choices.
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
