Computing fairness: ethics of modeling and simulation in public health

Issue/Problem The World Health Organisation (WHO) and Association of Schools of Public Health in the European Region (ASPHER) roadmap for the professionalisation of the public health workforce has noted ethics as a pillar for professionalisation. It also highlights the need for code of ethics and professional conduct for all public health professionals and institutions. The Global Network for Academic Public Health (GNAPH), in collaboration with EUPHA, ASPHER, and the UK Faculty of Public Health has been working together for a code of ethics. Description of the problem The WHO/ASPHER group asked for a framework to be developed by the expert group as part of the rollout of the professionalisation agenda. The objective of this work was to develop a guide that is applicable to different countries within Europe to develop their own of code of ethics and conduct. A modified Delphi approach to building consensus amongst professionals in ethics included law, academia, and public health professionals. The questions sought to be answered were what and how to develop a framework and what should be included in the guide for creating a public health code of ethics. Results Throughout this method we developed five values, and twelve principles that are essential for the development of a code of ethics through a consensus building exercise. This framework was the first of its kind as part of the professionalisation agenda for different regions. There will be ongoing work piloting this framework in practice with various professional groups and institutions. Lessons Throughout this process, the need and demand across the public health community for ethical education and development of code of ethics was made evident. Collaboration between academics, ethicist, lawyers, and public health professionals and policy makers were key in developing a public health code of ethics. Identifying the challenges with monitoring and evaluating of its implementation a public health code of ethics. Key messages • Ethics, and a code of ethics are an essential component of the professionalisation agenda of the public health workforce. • There is need for continued collaboration and partnership with public health professionals and institutions in enacting its implementation and evaluation.

In osteoarthritis, articular cartilage degenerates and eventually wears out, resulting in pain and disability. It is a major challenge in health care to prevent osteoarthritis or slow down the progression of the disease. The onset of osteoarthritis may result from an altered stress and strain state in cartilage, for example, due to an injury of ligament, cartilage, or meniscus. However, the disease progression is patient specific and can hardly be predicted. In order to assess the articular cartilage function and possible failure sites in joints, and to evaluate the onset and progression of osteoarthritis patient-specifically, computational models may become useful tools. Although there has been significant progress over the last years, these models need to be further developed before they may eventually become of direct clinical value. For clinical, patient-specific application of a computer model, a realistic description of joint geometry, joint kinematics, and tissue loading is ultimately required. Geometrical data can be obtained from imaging modalities, such as MRI, which become increasingly more detailed. Still, meshing of such information and running computational simulations of such meshes is challenging. Simulation analysis of joint kinematics is computationally expensive and requires knowledge of the kinematic constraints as a consequence of muscles and ligaments, including, for instance, pre stresses and attachment sites of ligaments. In many cases, 3D and/or kinematic simulations are still more feasible in combination with simpler material models for cartilage. More advanced material descriptions are required for a more detailed investigation of mechanical conditions inside cartilage. However, these are essential to move forward in our understanding of the relationship between mechanical loading, cartilage damage, and osteoarthritis. A wide variety of cartilage models have been developed and employed to evaluate the mechanical behavior at the cell, tissue, and joint level. They have been used to evaluate static and dynamic tissue behavior, to explore effects of mechanical and biochemical loading, and even to predict tissue adaptation at the long time scale. The expectation is that such models may be taken to the next level, where patient-specific characteristics are incorporated in 3D kinematic joint models. Given the composite structure of cartilage with the collagen fibril network, proteoglycan matrix, and fluid, this special issue has a focus on the development and application of fibril-reinforced models at different scales. A review of the fibril-reinforced tissue models and their application in the cell level is presented (P. Julkunen et al.). Practical considerations in modeling applications and model limitations are further discussed. A thorough review of recent advances in computational modeling of human knee joints is also presented (M. Kazemi et al.). Creation of knee joint models starting from imaging data (such as MRI) is summarized, clinical applications of the models are discussed, and examples are given for patient-specific evaluation of knee joint mechanics. Furthermore, influence of the altered depth-dependent properties of cartilage in osteoarthritis on fluid pressurization in a knee joint is demonstrated (Y. Dabiri and L. P. Li). Since generation of computational finite element models of knee joints is time-consuming and model simulation times can be relatively long, a semiautomated method is presented to study tibiofemoral contact stresses of adult subjects (D. D. Anderson et al.). Combination of experimental measurements and modeling is reviewed and model validation in cell, tissue, and joint level is discussed (P. Julkunen et al. and M. Kazemi et al.). Specifically, experimental results on the depth-dependent microstructural response of cartilage under directly loaded and unloaded regions are presented (A. Thambyah and N. Broom). Loading-related changes in chondron aspect ratios at different zones are also shown. These results can be used for the validation and development of computational models incorporating higher degrees of structural realism. Further, experimentally measured chondrocyte deformation behavior under mechanical loading of the tissue is presented for normal and osteoarthritic cartilage (P. Tanska et al.). Computational multiscale modeling is applied to explain differences in the cell behavior between the aforementioned groups. Finally, potential future directions in the application of multiscale models for the evaluation of cell responses and disease progression in knee joints are addressed (P. Julkunen et al.). Recent advances in the development of computational models of articular cartilage, as partially demonstrated in this special issue, and models of joints with realistic patient-specific material descriptions and joint kinematics have brought clinical application closer. More advanced adaptation models, likely combinations of models addressing different length scales, should ultimately lead to the prediction of altered tissue properties and wear, which strongly correlate to the development of osteoarthritis. Ultimately, computational modeling may become a clinical tool for identifying or optimizing patient-specific treatment strategies, such as rehabilitation and surgical interventions. Rami K. Korhonen Petro Julkunen LePing Li Corrinus C. van Donkelaar

Ethics work for good participatory action research: Engaging in a commitment to epistemic justice Participatory and responsive approaches to research strive to be democratic, inclusive and impactful. Participatory researchers share a commitment to epistemic justice and actively engage citizens and users as well as other stakeholders in the co-creation of knowledge for social change. While more and more researchers and policymakers feel attracted to these approaches in practice, the normative ideals of social inclusion and justice are sometimes hard to realize, because of established interests, power relations and system requirements. In this article I argue that participatory researchers and evaluators have a moral responsibility to do ‘ethics work’. This is more than just following ethical principles and codes of conduct. ‘Ethics work’ entails the labour and effort one puts into recognizing ethically salient aspects of situations, developing oneself as a reflexive practitioner, paying attention to emotions and relationships, collaboratively working out the right course of action and reflecting in the company of critical friends. In this article I present the theory and ethics of participatory approaches, illustrate ethical issues and ethics work related to collaboration, politics and power, and share lessons based on ten years of practice in the field of health and social well-being.

Advances in high-resolution, high-throughput measurement techniques, coupled with powerful recent developments in computational modeling, simulation, and artificial intelligence (AI), have enabled breakthroughs in rapid discovery and testing of novel materials. These possibilities raise new questions pertaining to the credibility of the underlying models for real-world use, especially when these models are adopted to inform engineering or regulatory decisions in applications that deal with human safety and life. We present a risk-informed credibility assessment framework, that has been applied in adjacent domains to support engineering and regulatory decision-making based on the use of computational (including AI) models, and demonstrate its application in the context of rapid discovery and testing of metallic alloys. The framework consists of a number of steps and best practices to define a question of interest, establish a context of use, assess the model risk, develop and execute a plan to validate and verify the model’s credibility within that context, document the results and deviations from the plan, determine adequacy of the models based on such results, and iterate over these steps until the required adequacy is achieved. We present a few case studies using a number of models developed to support rapid discovery and testing of novel metallic alloys in DARPA METALS. We highlight challenges, gaps, and opportunities for future directions that are generalizable to broader engineering applications.

On New Zealand's weak, strong and muddled management of a COVID-19 epidemic.

Hybrid simulation is an economical structural testing technique in which the critical part of the structure expected to respond in the inelastic range is tested physically whereas the rest of the structure is modelled numerically using a finite element analysis program. The article describes the development of a computational model for the hybrid simulation of the seismic collapse of a steel two-tiered braced frame structure due to column buckling. The column stability response in multi-tiered braced frames is first presented using a pure numerical model of the braced frame studied. The development of the hybrid simulation computational model is then discussed. Effects of initial out-of-straightness imperfections and axial stiffness, P-Delta analysis approach, and gravity analysis technique on the hybrid simulation results are evaluated using a numerical hybrid simulation model. Finally, the results of a continuous pseudo-dynamic hybrid simulation of the seismic response of the steel multi-tiered concentrically braced frame are presented. The test showed that failure of columns by instability is a possibility and can lead to collapse of multi-tiered braced frames, as was predicted by numerical analysis. Furthermore, suitable modeling methods are proposed for hybrid simulation of steel braced frame structures.

Transcranial direct current stimulation (tDCS) is a neuromodulatory technique that delivers low-intensity currents facilitating or inhibiting spontaneous neuronal activity. Such noninvasive electrotherapies have a number of advantages that have been exploited in clinical practice; in particular, tDCS dose is easily customized by varying electrode number, position, size, shape, and current. Recent developments in computational models have further customized dose to individual subjects and cases. Finite Element Method models developed from high-resolution magnetic resonance imaging (MRI) scans are among the tools available today. Designing and interpreting these models while aware of their limitations can affect the rational design of electrotherapy as evidenced in studies combining computer modeling and clinical data. Though modeling for noninvasive brain stimulation is still in its development phase, it is predicted that with increased validation, dissemination, simplification, and democratization of modeling tools, computational forward models of neuromodulation will become useful tools to guide the optimization of clinical electrotherapy. As an example of tDCS customization and dose design, case studies of computational modeling in susceptible populations are discussed in the final section.

Recent advances in computational modeling have led to significant discoveries about the representation and acquisition of phonological knowledge and the limits on language learning and variation. These discoveries are the result of applying computational learning models to increasingly rich and complex natural language data while making increasingly realistic assumptions about the learning task. This article reviews the recent developments in computational modeling that have made connections between fully explicit theories of learning, naturally occurring corpus data, and the richness of psycholinguistic and typological data possible. These advances fall into two broad research areas: ( a) the development of models capable of learning the quantitative, noisy, and inconsistent patterns that are characteristic of naturalistic data and ( b) the development of models with the capacity to learn hidden phonological structure from unlabeled data. After reviewing these advances, the article summarizes some of the most significant consequent discoveries.

The development of multimodality preclinical imaging techniques and the rapid growth of realistic computer simulation tools have promoted the construction and application of computational laboratory animal models in preclinical research. Since the early 1990s, over 120 realistic computational animal models have been reported in the literature and used as surrogates to characterize the anatomy of actual animals for the simulation of preclinical studies involving the use of bioluminescence tomography, fluorescence molecular tomography, positron emission tomography, single-photon emission computed tomography, microcomputed tomography, magnetic resonance imaging, and optical imaging. Other applications include electromagnetic field simulation, ionizing and nonionizing radiation dosimetry, and the development and evaluation of new methodologies for multimodality image coregistration, segmentation, and reconstruction of small animal images. This paper provides a comprehensive review of the history and fundamental technologies used for the development of computational small animal models with a particular focus on their application in preclinical imaging as well as nonionizing and ionizing radiation dosimetry calculations. An overview of the overall process involved in the design of these models, including the fundamental elements used for the construction of different types of computational models, the identification of original anatomical data, the simulation tools used for solving various computational problems, and the applications of computational animal models in preclinical research. The authors also analyze the characteristics of categories of computational models (stylized, voxel-based, and boundary representation) and discuss the technical challenges faced at the present time as well as research needs in the future.

This study investigates job satisfaction and its effects on organizational commitment, where procedural justice and distributive justice are used as mediation variables. The method used in this study is a case study on employees of educational institutions in Purwokerto. Questionnaires were given to 74 employees from the management level to the staff level as respondents. Data analysis technique used is the analysis of PLS (Partial Least Square) using SmartPLS 3.0. The results showed that Job Satisfaction has a significant effect on Procedural Justice and Distributive Justice, Procedural Justice has no significant effect on Commitment of Oganization, while Distributive Justice has a significant effect on Organizational Commitment. It also proves that distributive justice mediates the relationship between job satisfaction and organizational commitment, but procedural justice can not mediate the relationship between job satisfaction and organizational commitment. Keywords: distributive justice, job satisfaction, organizational commitment, procedural justice.

Q Maintaining my moral integrity is important to me, yet I have been faced with situations that challenge my ethical compass. How do I develop the internal strength to remain true to my values? How can my organization support efforts by my coworkers and me to speak up when we feel there are ethical concerns? What can I do as an individual and what can my organization do to enhance the environment to ensure ethical practice?A Karen Stutzer, rn, phd, and Mary Bylone, rn, msm, cnml, reply:It is well documented and acknowledged that the critical care environment contains daily challenges to individual nurses in regard to their moral integrity.1–3 Andrew Jameton4 initially described moral distress as when "one knows the right thing to do, but institutional constraints make it nearly impossible to pursue the right course of action."4(p6) Identifying moral distress was the beginning of decades of work that has described and measured the impact of moral distress across multiple nursing specialties.1 The debilitating impact of moral distress on nurses is supported by research and includes burnout, disengagement from patients and families, stress-related illnesses, turnover, and leaving the profession.1A promising approach to addressing situations that challenge one's ethical compass and the resulting moral distress is to incorporate strategies that promote moral resilience into organizational and individual practices. Moral resilience has been described as "the capacity of an individual to sustain or restore integrity in response to moral complexity, confusion, distress or setbacks."5(pS13) Developing personal strategies to strengthen one's approach to ethically distressing situations and seeking organizational strategies to support ethical practice are 2 ways to build moral resilience capacity.At the National Teaching Institute held in May 2017, a panel led several hundred acute and critical care nurses in a discussion about moral distress. Participants were encouraged to "forge a new path"6 and consider strategies to shift the dialogue and experience to those that promote resilience. This column is a reflection of the thinking of these participant experts—acute and critical care nurses who navigate the health care system to advocate for patients and families daily (Tables 1 and 2).Care for the critically ill patients occurs within health care organizations, and the ethical climate of those environments directly affects the ability of the nurse and members of the interprofessional team to provide care ethically.8 Health care organizations can and should provide systemic support to ensure the environment is conducive to ethical practice. The American Nurses Association Code of Ethics requires nursing leaders, in collaboration with nursing care providers, to ensure the practice environment supports the moral integrity of nurses.9,10Policies that support ethical practice are key to creating organizational infrastructure that can mitigate morally distressing events. Nursing leadership and bedside care providers need to identify the policies that are most supportive of ethical practice and develop those policies collaboratively. Among the areas that might be covered are mechanisms for addressing ethical concerns, protection of moral integrity, and organizational priorities for ethical practice. More specific examples include informed consent, withholding and withdrawal of life-sustaining therapies, health care decision-making, and calling an ethics consultation.11Nursing leaders are positioned to influence ethical practice through their words and deeds. Through support of shared governance structures, nurses are able to ensure their voice is included in policy development. It is key that policies and practice reinforce the expectation that nurses will verbalize concerns.12Organizations must provide adequate financial and human resources to support an ethical culture. The presence of effective ethics committees and qualified ethics consultants provides organizational structure for ethical practice.8 There should also be support for nurses who provide care to patients to be part of these committees. Organizations should provide processes, structures, and well-prepared educators to ensure ongoing education to develop and sustain ethical competence. Inquiring daily about potential ethical concerns in concert with the presence of well-prepared ethics resources can provide opportunity for early intervention and perhaps diminish the degree of distress experienced by all members of the team.13The emergence of moral resilience as a strategy to manage and cope with moral distress is a relatively new discussion. As one thinks about personal actions, it is an important step to recognize one's intrinsic ability to care for oneself, advocate for patients, and work with organizational leadership to address ethically distressing situations. The professional Code of Ethics supports the requirement that nurses must promote personal health and well-being, and advocate for patients and ethical work environments.9Moral resilience comes from a place of intentional actions to build on personal strengths. Knowing one's personal values and being willing to take courageous action move the experience from one of distress to one of resilience.5 The ability to stay true to oneself, make decisions that are consistent with one's beliefs, and persevere in addressing concerns are attributes of moral resilience12 and behaviors one can develop and nurture. Additionally, nurses must be willing to support others in these situations, even when the issue is not a problem for themselves. Knowing your colleagues are supportive of what is important to you provides strength for speaking up, which, in turn, improves the dialogue.Maintaining balance and harmony in one's life needs to be a priority. A healthy diet, proper rest, and time for relaxation can foster the needed energy to cope with distressing situations. Mindfulness practices, exercise, yoga, or other activities that encourage respite from stress can also enhance resilience.Membership in professional organizations should be considered a strategy to promoting moral resilience. Nursing organizations provide a collective voice for nursing concerns and issues, and provide opportunities for networking and supportive dialogue with colleagues. The American Nurses Association has convened a professional issues panel that examines moral resilience with the intention of identifying strategies to strengthen the ethical voice of nurses and, thereby, promote moral resilience.14 In addition to the development and publication of the Healthy Work Environment Standards,7 the American Association of Critical-Care Nurses is an active participant in collaborating with a variety of nursing organizations to promote moral resilience.3 More work is needed to make these documents part of every workplace.Moral resilience moves the internal dialogue from one of disappointment and powerlessness to one of strength and empowerment. Consciously acting to develop moral resilience enhances one's self-confidence in managing ethically difficult situations.12 Through an ongoing commitment to developing self-awareness and insight when faced with ethical challenges, one can gain a deeper sense of understanding and sense of mastery when faced with ethically distressing situations.5

While generative artificial intelligence (AI) may lead to technological advances in the mental health field, it poses safety risks for mental health service consumers. Furthermore, clinicians and healthcare systems must attend to safety and ethical considerations prior to deploying these AI-mental health technologies. To ensure the responsible deployment of AI-mental health applications, a principled method for evaluating and reporting on AI-mental health applications is needed. We conducted a narrative review of existing frameworks and criteria (from the mental health, healthcare, and AI fields) relevant to the evaluation of AI-mental health applications. We provide a summary and analysis of these frameworks, with a particular emphasis on the unique needs of the AI-mental health intersection. Existing frameworks contain areas of convergence (e.g., frequent emphasis on safety, privacy/confidentiality, effectiveness, and equity) that are relevant to the evaluation of AI-mental health applications. However, current frameworks are insufficiently tailored to unique considerations for AI and mental health. To address this need, we introduce the Readiness Evaluation for AI-Mental Health Deployment and Implementation (READI) framework for mental health applications. The READI framework comprises considerations of Safety, Privacy/Confidentiality, Equity, Effectiveness, Engagement, and Implementation. The READI framework outlines key criteria for assessing the readiness of AI-mental health applications for clinical deployment, offering a structured approach for evaluating these technologies and reporting findings.

The goal of the present paper is to model two classic lines of research in which employees starred, organizational justice and citizenship behavior (OCB), but that have never been studied together when targeting customers. The suggestion is made that a hotel’s fair treatment (in terms of distributive, procedural, and interactional justice) toward customers will be appreciated by the employees, who will reciprocate in kind by favoring the hotel with increased customer-oriented behaviors (COBs). Data were collected from 204 employees at eight upscale hotels in the Canary Islands (Spain). Unlike in the case of perceptions of distributive justice, results of structural equation modeling demonstrate that employees substantively react to interactional and procedural justice toward guests by engaging in customer-oriented behaviors (COBs). The findings offer new reasons why employees decide to engage in COBs, and they highlight potentially beneficial effects of fair treatment toward guests bring to hospitality through promoting COBs.

Purpose: This article aims to examine the criteria for notary accountability concerning the use of online media for notary promotion in Indonesia. Drawing on Gustav Radbruch's theory, the study analyzes the elements of intentionality, legal certainty, and the pursuit of profit, as outlined in the Notary Law and the Provisions of the Notary's Code of Ethics. Additionally, it explores the notion of the notary profession as an officium nobile, emphasizing its noble position and ethical responsibilities. Methods: The research methodology involves a theoretical analysis based on Gustav Radbruch's legal theory and relevant provisions of Indonesian law pertaining to notary accountability. Through a qualitative examination, the study delves into the criteria for notary accountability, particularly concerning the use of online media for promotional purposes. Legal principles and ethical considerations are applied to evaluate the implications of notary actions in the digital age. Results and Discussion: The analysis reveals that the criteria for notary accountability encompass elements of legal certainty, intentionality, and the pursuit of profit, as delineated in legal frameworks and ethical codes. Maintaining the noble position of the notary profession entails upholding ethical standards and adhering to legal norms, ensuring fairness and justice in notarial practices. The discussion emphasizes the need for holistic and ethical examinations in cases of alleged violations, to uphold the principles of justice and accountability. Implications of the Research: The findings of this study have implications for notarial practice in Indonesia, highlighting the importance of ethical conduct and legal compliance, particularly in the context of online media usage for promotional activities. By elucidating the criteria for notary accountability, the research contributes to enhancing professional standards and fostering public trust in notarial services. Originality/Value: This article contributes to the literature by examining the criteria for notary accountability within the framework of online media promotion, drawing on Gustav Radbruch's legal theory and Indonesian legal provisions. By addressing the ethical and legal considerations inherent in notarial practices, the study provides valuable insights into the evolving landscape of notarial accountability in the digital age, thus adding to the body of knowledge on professional ethics and legal responsibility.

In this research, we designed a universal graph model. We targeted this work on improving graph data organization representation, flexible formation of graph substructures for complex system modeling and generic processing of data organization. We form coherently composed graph modeling features including inheritance hierarchy of basic graph types, data participating graphs of multiple aspects, hierarchically composed and connected graphs, and property-based graph models based on existing graph models. Design cases are used to illustrate its achievement of our design goal. Keywords—graph theory; graph model, multi-aspect graph; hierarchical graph I. INTRODUCTION Computer modeling plays a key role to apply computer's capability in various fields of applications. Effective support of computer modeling should consist of data representation capability, data access and navigation capability, and generic operations for forming more general and reusable generic algorithms. As computer applications keep growing complex in wider areas, we need enhanced computer models capable of supporting these applications. Graph data models are one of the most extensively used discrete mathematical models in computer modeling of various applications from VLSI circuit design, computer system design and modeling, network modeling, etc. Traditional graph libraries such as LEDA(1), Boost Graph Library(2), LINK(3), and GraphStream(4) focuses on single- level directed and undirected graph modeling. Lee's work(5) utilized decorator and visitor patterns to add new vertex data and attach processing during graph traversal. Pizzonia's (6) employed static object-oriented extension of graph property classes associated with corresponding graph operations. Bertault(7) employed an extensible graph property class hierarchy and dynamic property checking. Directed and undirected hypergraphs are not supported in these approaches. Hyperedges in hypergraphs can be represented as vertices in ordinary graphs. Hypergraphs cannot be treated as super types of directed and undirected graphs to support generic algorithms with ordinary graphs. On data organization point of view, these approaches provide various graph organization types but only as single- level specialized graph type. Each of them should be utilized distinctly. Once these graph models need to be combined in a complex data model, designers should add artificial data structures to join them together. Still, such combined data model or its selected subparts cannot be viewed and utilized as if in a single graph model. This requires designers to add more application-specific gluing data structures, processing codes of distinct graph substructures and their gluing logic, and semantically similar but redundant non-generic codes. It thus hinders effective development and reuse of computer models for various complex applications. From our literature survey, we did not find any previous work providing combinational features for flexibly composing graph structures. Our design goal lies in three improvement directions, representation of complex system data organization, flexible formation of graph substructures for processing, and generic processing of data organization as well as individual data. Our main approach is to form coherently composed graph modeling features including inheritance hierarchy of basic graph types, vertex data participating in graphs of multiple aspects, hierarchically composed and connected graphs, and property-based graph models based on existing graph models. It forms a universal graph model of combinations of complex graph-oriented data organizations, flexibly forms various virtual graph substructures for complex system modeling and processing, and support generic processing capability at the graph structure levels and at the leaf data levels. In section 2, we describe refined design goals and the approach of our work. In the following sections, we in sequence introduce planned composed graph modeling features of our universal graph model, including generic accesses among basic graph models, joining multiple graphs into a multi-aspect graph model, generic structuring of graph models, and generic accesses of property-based graph models. In section 7, we introduce its software design and a design case. The last section concludes this research.