Health Care Justice Research Articles

A COVID question: the impact on our nursing home residents

COVID-19, a novel coronavirus which targets the respiratory system, has had a significant impact on Irish nursing homes. The virus was declared a global pandemic in March 2020, and as of 14th June the Health Protection Surveillance Centre has reported 258 clusters of cases in nursing homes, comprising 21% of all cases in Ireland and associated with 943 deaths. This pandemic is a source of many ethical dilemmas — an example being the ethical issues arising from enforced social isolation imposed on residents during lock-down. The cocooning of residents in nursing homes raises a question with no simple answer — does the risk this isolation poses to the wellbeing of elderly residents outweigh the benefits of such strict measures to protect them from COVID-19? The adverse effects of isolation, although relevant in any infectious outbreak, are compounded in this instance by the unprecedented scale of the coronavirus pandemic and the immense pressure on the healthcare system as it faces this new threat to health. Even in the chaos of a global pandemic, the ethical principles at the root of all healthcare decisions, namely non-maleficence, beneficence, autonomy, and justice remain pertinent.

Violence in contact and custody proceedings-Acase of child endangerment in the family court-Where are efficient child protection measures?

Against the background of 30years of discussion in medicine and law about unfounded accusations and the existence of reasonable suspicion of intra-family violence in contact and custody proceedings, this article emphasises the importance of focusing on child protection by all professionals.Based on acase report in connection with the Federal Ministry of Justice's handout "Dealing with violence in custody and contact proceedings", the article describes the relevance of child protection in Austrian case law and the means by which it is undermined. In the name of science, ideological diagnoses are used instead of evidence-based treatment diagnoses [1]. "Professionals from the healthcare system, youth welfare, justice and education should cooperate in child protection with the aim of recognising, identifying and ending child abuse, maltreatment and/or neglect as such (cf. §3 KKG) [2]."In legal practice, however, practitioners in the psychosocial, psychosomatic and psychotherapeutic fields are often not recognised as witnesses because of the assumption they are influenced by parents or patients themselves. In Austria, the evaluation of evidence is the responsibility of the judge and it is often assumed that practitioners cannot take an objective position towards their patients and their relatives. Specialist medical or psychological experts are therefore of crucial importance. When taking their oath, they have committed themselves to objectivity and impartiality in the recording of findings and orientation towards scientific principles and standards ("state of the art") and their application, as well as to continuous further training and education in the preparation of expert opinions [3].The specific case in question is achild with adiagnosis of post-traumatic stress disorder following suspected sexualised violence, enuresis and enkopresis in connection with the traumatic experiences.The connection between violent situations and child and adolescent psychiatric disorders and the course of the custody and contact proceedings are described. Relevant legal interests such as the will of the child, the best interests of the child, attachment tolerance and protection against violence are addressed and put up for discussion. Topics such as domestic, psychological, sexualised, institutional violence and intimate terror are presented.The case report is an example of how child psychiatrists deal with points relevant to the Federal Ministry of Justice's handbook "Dealing with violence in custody and contact proceedings", which was published in January 2024. It represents aguideline for the area of court proceedings, assessment and treatment.

Ethical Artificial Intelligence Framework Theory (EAIFT): A New Paradigm for Embedding Ethical Reasoning in AI Systems

The rapid development of artificial intelligence (AI) has created several ethical issues, including bias, a lack of transparency, and privacy concerns, demanding the incorporation of ethical governance directly into AI systems. This study introduces the Ethical Artificial Intelligence Framework Theory (EAIFT), a novel approach to incorporating ethical reasoning into AI. It emphasizes real-time oversight, open decision-making, bias detection, and the ability to change ethical and legal norms. EAIFT advocates for establishing "ethical AI watchdogs" that automatically monitor and ensure the ethical operation of AI systems, together with dynamic compliance algorithms that can adapt to regulatory changes. The paradigm also encourages transparency and explainability to build user trust and detect and correct biases to ensure fairness. This paper employs a qualitative methodology that combines stakeholder interviews, content analysis, and expert commentary to evaluate EAIFT's potential to increase ethical accountability in various areas, including healthcare, banking, and criminal justice. The findings suggest that EAIFT outperforms existing ethical frameworks by proactively reducing biases, increasing transparency, and ensuring adherence to ethical standards. While presenting a comprehensive and adaptable technique, the study also acknowledges limitations in empirical testing and the need for additional research to widen EAIFT's applicability to future ethical challenges in artificial intelligence. The paper suggests future research subjects, such as empirical testing in different scenarios, a more in-depth examination of ethical risks, and the inclusion of the framework into new AI technologies to promote responsible AI governance by societal norms and values.

The experiences of medical and pharmacy students participating in a collaborative online international learning on social accountability and global health: a qualitative study

BackgroundSocial accountability aims to promote a collective ethic that upholds the fundamental values of equity, efficiency, solidarity, and social justice in healthcare and is now considered as a critical mission of academic health centers. Collaborative Online International Learning (COIL) is a pedagogical approach that uses digital technology to provide experiential international learning, specifically for increasingly diverse and multicultural healthcare work environments. The SOLID’AIRS program is an innovative French-language COIL that aims to set up international exchanges and workshops on social accountability between health sciences students. The aim of our study was to assess the impact of participation in the SOLID’AIRS program on medical and pharmacy students.MethodFive universities in four different countries were involved in SOLID’AIRS. We conducted a qualitative study by performing individual, semi-directed interviews with students who participated in the program from 2021 to 2022. A thematic analysis was conducted in five chronological phases: (1) reading, (2) descriptive coding, (3) conceptual coding, (4) identification of themes and (5) production of a coherent thematic structure.ResultsAfter including sixteen student participants, 13 medical and 3 pharmacy students, we identified four main themes related to the impact of participation: (1) previous experiences in social accountability and international learning, (2) perception of the program, (3) perceived impacts of the program, and (4) difficulties encountered and avenues to improve the program. Overall, the program was well received by all participants who reported the advantages and limitations of the online learning format. The primary advantage of this format was its feasibility. The participants noted both professional and personal benefits of the program for their current and future practice, including greater reflexivity towards health sciences practice. Based on the challenges faced during the program, particularly in coordinating group work and communication, participants suggested increased supervision of group projects by collaborators, and organizing at least one in-person meeting for future editions. The participants reported encountering difficulties during the COIL and suggested ways of improvement.ConclusionParticipating in a COIL on social accountability appears to be an effective way to adopt a reflective approach to medical practice and should be implemented and evaluated in other educational contexts.

Methods to advance health equity and social justice in healthcare: Protocol for a scoping review on the utilisation of routinely collected data.

Many areas of healthcare are impacted by a paucity of research that is translatable to clinical practice. Research utilising real-world data, such as routinely collected patient data, may be one option to efficiently create evidence to inform practice and service delivery. Such studies are also valuable for exploring (in)equity of services and outcomes, and benefit from using non-selected samples representing the diversity of the populations served in the 'real world'. This scoping review aims to identify and map the published research which utilises routinely collected clinical healthcare data. A secondary aim is to explore the extent to which this literature supports the pursuit of social justice in health, including health inequities and intersectional approaches. This review utilises Arksey and O'Malley's methodological framework for scoping reviews and draws on the recommended enhancements of this framework to promote a team-based and mixed methods approach. This includes searching electronic databases and screening papers based on a pre-specified inclusion and exclusion criteria. Data relevant to the research aims will be extracted from included papers, including the clinical/professional area of the topic, the source of data that was used, and whether it addresses elements of social justice. All screening and reviewing will be collaborative and iterative, drawing on strengths of the research team and responsive changes to challenges will be made. Quantitative data will be analysed descriptively, and conceptual content analysis will be utilised to understand qualitative data. These will be collectively synthesised in alignment to the research aims. Our findings will highlight the extent to which such research is being conducted and published, including gaps and make recommendations for future endeavours for real-world data studies. The findings from this scoping review will be relevant for practitioners and researchers, as well as health service managers, commissioners, and research funders.

Relevance of learning health systems to physiatrists and its synergy with implementation science: A commentary.

As health care attempts to bridge the gap between evidence and practice, the concept of the learning health system (LHS) is becoming increasingly relevant. LHS integrates evidence with health systems data, driving health care quality and outcomes through updates in policy, practice, and care delivery. In addition, LHS research is becoming critically important as there are several initiatives underway to increase research capacity, expertise, and implementation, including attempts to stimulate increasing numbers of LHS researchers. Physical Medicine & Rehabilitation (PM&R) physicians (physiatrists), nurses, therapists (physical therapists, occupational therapists, speech therapists, clinical psychologists), and scientists are affiliated with LHSs. As LHS research expands in health care systems, better awareness and understanding of LHSs and LHS research competencies are key for rehabilitation professionals including physiatrists. To address this need, the Agency of Healthcare Research and Quality (AHRQ) identified 33 core competencies, grouped into eight domains, for training LHS researchers. The domains are: (1) Systems Science; (2) Research Questions and Standards of Scientific Evidence; (3) Research Methods; (4) Informatics; (5) Ethics of Research and Implementation in Health Systems; (6) Improvement and Implementation Science; (7) Engagement, Leadership, and Research Management; and the recently added (8) Health and Healthcare Equity and Justice. The purpose of this commentary is to define LHS and its relevance to physiatrists, present the role of implementation science (IS) in LHSs and application of IS principles to design LHSs, illustrate current LHS research in rehabilitation, and discuss potential solutions to improve awareness and to stimulate interest in LHS research and IS among physiatrists in LHSs.

Evaluation of Explainable Artificial Intelligence using TOPSIS Method

Explainable Artificial Intelligence (XAI) refers to the development of AI systems are transparent, explainable and their comprehensible for results can provide explanations or predictions. As AI technologies, particularly machine learning models, become more complex and sophisticated, there is a growing need to ensure that their decisions can be comprehended and trusted by humans, especially health, Finance and such as criminal justice in important domains. Evaluating Explainable Artificial Intelligence (XAI) is essential to ensure transparency, accountability, and user trust in AI systems. Interpretability is a key factor, examining how easily the model's internal mechanisms can be understood. Model transparency, feature importance, and the clarity of visualizations contribute to this aspect. Differentiate between post-hoc and intrinsic explanations, considering whether the model inherently provides interpretable insights. The distinction between local and global explanations is crucial, as it determines whether explanations focus on individual predictions or the overall model behavior. Robustness and consistency are assessed through stability and sensitivity analysis, ensuring that explanations remain reliable across similar instances. Additionally, ethical considerations, such as fairness and transparency in decision-making, must be addressed to uncover and mitigate biases. User feedback and the relevance of explanations to the specific use case contribute to a comprehensive evaluation, fostering the development of XAI systems that are not only technically robust but also ethically sound and user-friendly. The significance of research in Explainable Artificial Intelligence (XAI) lies in addressing critical challenges associated with the adoption and deployment of AI systems in various domains. As AI technologies, particularly complex machine learning models, become integral to decision-making processes in areas such as healthcare, finance, and criminal justice, the need for transparency and interpretability becomes paramount. Topsis involves optimizing from an advantageous standpoint by simultaneously minimizing the distance to and maximizing the distance from a reference point, which is defined in relation to solutions within a set of alternative options and numerous identification criteria. The importance of Topsis criteria lies in the potential to integrate comparative weights. This study conducts a comprehensive review of Topsis, exploring various weighing schemes and employing different distance measurements. Numerous applications of Topsis are examined, particularly its utilization in comparing results for a diverse set of multiple criteria data with varying weights. Interpretable Machine Learning Models, Human-Centric Design in XAI, Ethical Implications of XAI, Industry-specific Applications of XAI and Hybrid Approaches for Model Interpretability. Interpretability Metrics, Human-Subjective Evaluation, Algorithmic Robustness and Real-world Impact. the Ranking of Evaluation Explainable Artificial Intelligence. Industry-specific Applications of XAI is got the first rank whereas is the Ethical Implications of XAI is having the Lowest rank.

THE EFFECT OF PSYCHOLOGİCAL SAFETY LEVELS OF HEALTH WORKERS ON THEİR PERCEPTİON OF ORGANİSATİONAL JUSTİCE

This study aims to determine the impact of psychological safety levels on healthcare workers' perceptions of organisational justice. The sample consists of 271 healthcare professionals who voluntarily participated in the study. Data were collected online. The study employed a questionnaire as a data collection tool and measured psychological safety and organisational justice using appropriate scales. To determine differences in perceptions of psychological safety and organisational justice among employees based on demographic variables, t-tests and ANOVA tests were conducted. Additionally, regression analysis was performed to investigate the impact of psychological safety on organisational justice perception. The data was analysed using the SPSS software package. The study found that the psychological safety perceptions of healthcare workers were above average and did not vary based on marital status, gender, or age. Additionally, employees' perceptions of organizational justice were slightly below average and did not vary based on the type of institution. The study also revealed a significant relationship between the psychological safety perceptions of healthcare workers and organizational justice, including its sub-dimensions. Health managers and human resources professionals should develop strategies to support employees' psychological safety and improve their perceptions of justice.

Participatory Action Research, Critical Adult Education, and the Work of D.E. Smith for Research Praxis

From 2017-2021, the Youth Action Research Revolution (YARR) team documented the institutional histories of young people experiencing homelessness in Canada. Interviews with youth focused on educational, child welfare, healthcare, and criminal justice institutions. Situated at the intersections of critical adult education (CAE), participatory action research (PAR), and Institutional Ethnography (IE), we outline our mobilization of IE to ground learning and action within our team. We document the different phases of learning that we undertook, as a means of illustrating how CAE, PAR, and IE can be mutually supportive frameworks for praxis and activist learning. We highlight our use of IE to bring illuminate and resist the institutionalizing processes at work in post-secondary contexts and reflect on the importance of mutual aid as essential to realizing the social justice potential of participatory research. We suggest that co-creating IE research with young adults with lived experiences of homelessness constitutes a unique opportunity to mobilize CAE values and tangibly support community research that seeks to positively influence the lives of those implicated by the problems we study together.

A critical and systematic literature review of epistemic justice applied to healthcare: recommendations for a patient partnership approach.

Invalidation from healthcare practitioners is an experience shared by many patients, especially those marginalized or living with contested conditions (e.g., chronic pain, fibromyalgia, etc.). Invalidation can include not taking someone's testimony seriously, imposing one's thoughts, discrediting someone's emotions, or not perceiving someone's testimony as equal and competent. Epistemic injustices, that is, the disqualification of a person as a knower, are a form of invalidation. Epistemic injustices have been used as a theoretical framework to understand invalidation that occurs in the patient-healthcare provider relationship. However, to date, the different recommendations to achieve epistemic justice have not been listed, analyzed, nor compared yet. This paper aims at better understanding the state of the literature and to critically review possible avenues to achieve epistemic justice in healthcare. A systematic and critical review of the existing literature on epistemic justice was conducted. The search in four databases identified 629 articles, from which 35 were included in the review. Strategies to promote epistemic justice that can be applied to healthcare are mapped in the literature and sorted in six different approaches to epistemic justice, including virtuous, structural, narrative, cognitive, and partnership approaches, as well as resistance strategies. These strategies are critically appraised. A patient partnership approach based on the Montreal Model, implemented at all levels of healthcare systems, seems promising to promote epistemic justice in healthcare.

Artificial intelligence in maxillofacial and facial plastic and reconstructive surgery.

To provide a current review of artificial intelligence and its subtypes in maxillofacial and facial plastic surgery including a discussion of implications and ethical concerns. Artificial intelligence has gained popularity in recent years due to technological advancements. The current literature has begun to explore the use of artificial intelligence in various medical fields, but there is limited contribution to maxillofacial and facial plastic surgery due to the wide variance in anatomical facial features as well as subjective influences. In this review article, we found artificial intelligence's roles, so far, are to automatically update patient records, produce 3D models for preoperative planning, perform cephalometric analyses, and provide diagnostic evaluation of oropharyngeal malignancies. Artificial intelligence has solidified a role in maxillofacial and facial plastic surgery within the past few years. As high-quality databases expand with more patients, the role for artificial intelligence to assist in more complicated and unique cases becomes apparent. Despite its potential, ethical questions have been raised that should be noted as artificial intelligence continues to thrive. These questions include concerns such as compromise of the physician-patient relationship and healthcare justice.

The concept of intersectionality in bioethics: a systematic review

BackgroundIntersectionality is a concept that originated in Black feminist movements in the US-American context of the 1970s and 1980s, particularly in the work of feminist scholar and lawyer Kimberlé W. Crenshaw. Intersectional approaches aim to highlight the interconnectedness of gender and sexuality with other social categories, such as race, class, age, and ability to look at how individuals are discriminated against and privileged in institutions and societal power structures. Intersectionality is a “traveling concept”, which also made its way into bioethical research.MethodsWe conducted a systematic review to answer the question of where and how the concept of intersectionality is applied in bioethical research. The PubMed and Web of Science databases were systematically searched and 192 articles addressing bioethical topics and intersectionality were finally included.ResultsThe qualitative analysis resulted in a category system with five main categories: (1) application purpose and function, (2) social dimensions, (3) levels, (4) health-care disciplines and academic fields, and (5) challenges, limitations, and critique. The variety of academic fields and health-care disciplines working with the concept ranges from psychology, through gynaecology to palliative care and deaf studies. Important functions that the concept of intersectionality fulfils in bioethical research are making inequities visible, creating better health data collections and embracing self-reflection. Intersectionality is also a critical praxis and fits neatly into the overarching goal of bioethics to work toward social justice in health care. Intersectionality aims at making research results relevant for respective communities and patients, and informs the development of policies.ConclusionsThis systematic review is, to the best of our knowledge, the first one to provide a full overview of the reference to intersectionality in bioethical scholarship. It creates a basis for future research that applies intersectionality as a theoretical and methodical tool for analysing bioethical questions.

Reproductive justice in pediatric health care: a scoping review protocol.

The objective of this scoping review is to identify the barriers and facilitators of reproductive justice in pediatric health care. Reproductive justice is defined as the right to maintain personal bodily autonomy, to have or not have children, and to parent children in safe and sustainable communities. The reproductive justice framework is often applied to adult women in conventional care settings; however, the need for health care guided by the framework should extend to all females of reproductive age in all care settings, including pediatric settings. This review will consider studies from 1994 to the present that report on the barriers and facilitators of reproductive justice in pediatric health care. Studies from any setting or geographic location will be included. This scoping review will include pediatric patients up to 21 years of age of any gender identity or sexual orientation who may birth a child, and their health care providers. Database searches will include CINAHL (EBSCOhost), MEDLINE (PubMed), Embase (Elsevier), and Web of Science Core Collection. Sources of unpublished studies and gray literature to be searched include MedNar and ProQuest Dissertation and Theses Science and Engineering Collection (ProQuest). The JBI methodology for scoping reviews will be followed. Data extracted will include details about the title, authors, year of publication, type of evidence, participants, context, and concept. The extracted data will be presented in diagrammatic or tabular format in a manner that aligns with the objective and questions of the scoping review. Open Science Framework https://osf.io/d5vf9.

Global Equity in Clinical Trials: An ASCO Policy Statement.

ASCO is a global professional society representing more than 50,000 physicians, other health care professionals, and advocates in over 100 countries specializing in cancer treatment, diagnosis, prevention, and advocacy. ASCO strives, through research, education, and promotion of the highest quality of patient care, to create a world where cancer is prevented or cured, and every survivor is healthy. In this pursuit, health equity remains the guiding institutional principle that applies to all its activities across the cancer care continuum. This ASCO policy statement emphasizes the urgent need for global equity in clinical trials, aiming to enhance access and representation in cancer research as it not only improves cancer outcomes but also upholds principles of fairness and justice in health care.

Anti-racist interventions to reduce ethnic disparities in healthcare in the UK: an umbrella review and findings from healthcare, education and criminal justice

ObjectivesTo assess the evidence for anti-racist interventions which aim to reduce ethnic disparities in healthcare, with a focus on implementation in the UK healthcare system.DesignUmbrella review.Data sourcesEmbase, Medline, Social Policy...

BiasTrap: Runtime Detection of Biased Prediction in Machine Learning Systems

Machine Learning (ML) systems are now widely used across various fields such as hiring, healthcare, and criminal justice, but they are prone to unfairness and discrimination, which can have serious consequences for individuals and society. Although various fairness testing methods have been developed to tackle this issue, they lack the mechanism to continuously monitor ML system behaviour at runtime. In this study, a runtime verification tool called BiasTrap is proposed to detect and prevent discrimination in ML systems. The tool combines data augmentation and bias detection components to create and analyse instances with different sensitive attributes, enabling the detection of discriminatory behaviour in the ML model. The simulation results demonstrate that BiasTrap can effectively detect discriminatory behaviour in ML models trained on different datasets using various algorithms. Therefore, BiasTrap is a valuable tool for ensuring fairness in ML systems in real-time.

Prediction and explainability in AI: Striking a new balance?

The debate regarding prediction and explainability in artificial intelligence (AI) centers around the trade-off between achieving high-performance accurate models and the ability to understand and interpret the decisionmaking process of those models. In recent years, this debate has gained significant attention due to the increasing adoption of AI systems in various domains, including healthcare, finance, and criminal justice. While prediction and explainability are desirable goals in principle, the recent spread of high accuracy yet opaque machine learning (ML) algorithms has highlighted the trade-off between the two, marking this debate as an inter-disciplinary, inter-professional arena for negotiating expertise. There is no longer an agreement about what should be the “default” balance of prediction and explainability, with various positions reflecting claims for professional jurisdiction. Overall, there appears to be a growing schism between the regulatory and ethics-based call for explainability as a condition for trustworthy AI, and how it is being designed, assimilated, and negotiated. The impetus for writing this commentary comes from recent suggestions that explainability is overrated, including the argument that explainability is not guaranteed in human healthcare experts either. To shed light on this debate, its premises, and its recent twists, we provide an overview of key arguments representing different frames, focusing on AI in healthcare.

Implementation Model for Motivational Interviewing Training in a Community College Human Services Program

Motivational interviewing (MI) is an evidence-supported style of communication used across many fields of human services, including addiction treatment, social work, healthcare, education, and criminal justice. MI can be utilized by a variety of human service professionals and requires no special licensure or educational degree. In 2016, our Human Services program at LaGuardia Community College, CUNY began to implement MI training as a core part of our curriculum to provide students with this micro-credential to increase their career readiness. This paper outlines our ongoing approach for implementing MI in a community college human services program, including the training model we used for preparing faculty and staff to implement this approach, as well as some reflections from graduates.

The government wage bill: Employment and compensation trends in South Africa

ABSTRACT This paper reviews government employment and compensation trends using data derived from the government's payroll. Employment levels and average pay grew strongly in the 2000s, but in the last decade, headcounts have stagnated or fallen, and increases in average pay have been moderate. The distribution of pay between government employees is compressed, especially when compared to income inequalities in society. The paper suggests attempts to reduce government's wage bill that rely on blunt, across-the-board measures will have their largest impact on core public services – basic education, healthcare and criminal justice – and are likely to erode the value of these services and the quality of service delivery.

Abstract IA013: Using genetic ancestry in genomics of tumor evolution

Abstract The burgeoning field of genomic science, particularly in the context of cancer research, has undergone a transformative shift with the incorporation of genetic ancestry in understanding tumor evolution. The work of Melissa B. Davis highlights this paradigm shift, revealing how biases within genomic data, primarily due to the underrepresentation of diverse ethnic groups, have historically influenced our understanding of cancer disparities. Davis's research emphasizes the critical importance of including diverse ethnic groups in genomic studies, not merely as a token inclusion but as a rigorous scientific necessity. Her research demonstrates that by integrating genetic ancestry into the study of tumor biology, we can gain a more comprehensive understanding of cancer's evolution and behavior. This approach is instrumental in identifying unique genetic markers and pathways influenced by ancestry, which may contribute to differences in cancer incidence, progression, and treatment response among different populations. Davis's work thus represents a significant stride towards personalized medicine, where treatment strategies can be tailored not just to the individual's genetic makeup but also to their ancestral background. This shift in focus towards a more inclusive genomic science has the potential to bridge the gap in cancer care disparities. By acknowledging and addressing these disparities, research spearheaded by scientists like Davis paves the way for more equitable healthcare outcomes. The inclusion of genetic ancestry in the study of tumor evolution is more than a scientific advancement; it's a step towards justice and equity in healthcare, ensuring that all groups benefit equally from the advancements in cancer research and treatment. Citation Format: Melissa B. Davis. Using genetic ancestry in genomics of tumor evolution [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA013.