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This chapter introduces the issue of the International Journal of Bioethics and Ethics of Science that deals with the gift and utilization of body parts and substances of human origin in human health care. I notably emphasize, in this introduction, the idea that care ethics, in the variant developed by Paul Ricoeur, provides a framework that is peculiarly suitable for the treatment of the ethical stakes associated with the field of therapeutic activities so delineated. I also emphasize, on the basis of two relevant practical cases, the fundamental importance of the organization of the health care system, both factually and in terms of individual and collective responsibility. I devote, finally, a third section to a synthetic presentation of the contributions to this volume. The latter are structured by three great interdependent types of stakes : the epistemological stakes relative to the basic distinction between body and mind ; the ethical stakes centered, notably, on the norm of non-remuneration ; and the political stakes shaped, in particular, by the norm of self-sufficiency and the trade-off between self-sufficiency and non-remuneration. Cet article introduit le volume du Journal International de Bioéthique et d’Ethique des Sciences qui traite du don des parties du corps et de substances d’origine humaine à des fins thérapeutiques. On soutient notamment, dans cette introduction, l’idée que l’éthique du soin, dans la variante développée par Paul Ricoeur, fournit un cadre particulièrement approprié pour le traitement des questions éthiques associées au domaine d’activités thérapeutiques ainsi caractérisé. On souligne, en second lieu, en s’appuyant sur deux cas pratiques pertinents, l’importance déterminante de l’organisation du système de soins, tant factuellement qu’en termes de responsabilité individuelle et collective. On consacre, enfin, une troisième partie à une présentation synthétique des contributions à ce volume. Celles-ci s’organisent autour de trois grands pôles interdépendants : enjeux épistémologiques relatifs à la distinction entre corps et esprit ; enjeux éthiques centrés, notamment, sur la norme de non-rémunération ; et enjeux politiques, qui prennent forme, en particulier, autour de la norme d’autosuffisance et de sa mise en tension avec la norme de non-rémunération.

Artificial intelligence (AI) systems are now prevalent in our daily lives and hold promise for transforming high-stakes fields such as healthcare. Medical AI systems are showing significant potential to support diagnostics and treatment recommendations. As these systems play an increasingly significant role in clinical decision-making, ensuring transparency in their design, operation, and outcomes is essential for building trust among key stakeholders, including patients, providers, developers, and regulators. However, many systems still function as "black boxes," making it challenging for users-such as clinicians, patients, and other stakeholders-to interpret and verify their inner workings. Here, we examine the current state of transparency in medical AIs, identifying key challenges and risks these opaque systems pose. After motivating the need for transparency in all aspects of the machine learning pipeline, from training data to model development to model deployment, we explore a range of techniques that promote explainability throughout the pipeline while highlighting the importance of continual monitoring and system updates to ensure that AI systems remain reliable over time. Finally, we address the need to overcome barriers that inhibit the integration of transparency tools into clinical settings and review regulatory frameworks that prioritize transparency in emerging AI systems. Through this survey, we aim to increase awareness of current challenges and offer actionable insights for stakeholders, such as researchers, clinicians, and regulators, on how to build trustworthy and ethically responsible AI healthcare solutions.

Over the last years, both in the popular press, policy and business circles and in academia, people call for corporations to orient their behavior towards 'purpose'. This is meant as a move away from shareholder value maximization as the lodestar for corporate action. But purpose-advocates are torn between two directions in thinking about corporate governance: towards corporate governance on behalf of stakeholders by an independent board, and towards corporate governance by stakeholders through a responsive board. The paper's aim is to enlighten this choice by placing it in a trilemma with a third option: corporate governance on behalf of shareholders. This corporate governance trilemma shows us which trade-offs are at stake in making choices between the relevant values: the minimization of externalities, collective decision-making costs and agency costs. It discusses the various trade-offs in the trilemma. Finally, the paper argues that corporate purpose is best served by a balance between board independence and responsiveness.

Programmatic assessment offers a system-level approach to evaluating students' competence by integrating multiple low-stakes assessments, longitudinal evidence and expert judgement. Although widely adopted across several health education disciplines in Australia, radiography education providers have not implemented programmatic assessment at a programme or course level. This paper proposes a radiography-specific programmatic assessment framework. The objective is to translate core programmatic assessment principles into curriculum design strategies that strengthen feedback, improve the defensibility of decisions and enhance national workforce readiness. The paper outlines key purposes of programmatic assessment in undergraduate radiography education including supporting learning, strengthening feedback mechanisms, tracking developmental progress and enabling defensible decisions grounded in longitudinal evidence. Critical design considerations include aligning assessments with a capability framework, generating evidence across diverse clinical contexts, prioritising narrative feedback and using portfolios as central evidence repositories. The analysis highlights the importance of competence committees for high-stakes decisions and the need to support shared assessment practices across varied clinical placement environments. The proposed radiography model integrates six components: capability framework, evidence generation, evidence aggregation, interpretation, decision-making and system learning. This model addresses radiography's multimodality workflow, training variation across sites and accreditation requirements for fairness, transparency and systematic monitoring. Programmatic assessment offers a coherent approach to strengthening radiography education by supporting clearer insight into learner development and ensuring consistent evidence of capability achievement across clinical environments. When adapted to radiography's multimodality practice and evolving workforce demands, programmatic assessment enhances readiness for independent practice and supports continuous curriculum improvement. Programmatic assessment provides a coherent framework for evaluating diagnostic radiography students’ professional capability by integrating longitudinal, narrative-rich evidence across clinical and simulated learning environments.Aligning assessment design with the Medical Radiation Practice Board of Australia (MRPBA) Professional Capabilities enables transparent, defensible progression decisions that evidence accreditation requirements while supporting learner development.Effective implementation of programmatic assessment in radiography depends on deliberate system design, including balanced assessment stakes, structured portfolios, assessor calibration and collective decision-making through competence committees.

Public health emergencies such as pandemics, natural disasters, and epidemics may require rapid, high-stakes decisions often made by elected officials with limited public health training. Artificial intelligence (AI) holds significant promise to enhance the quality, transparency, and timeliness of governmental decision-making during such crises. This paper examines the potential of AI as a decision-support tool for elected officials while identifying key technical, logistical, ethical, and policy challenges. Technical considerations include model accuracy, data representativeness, and privacy protection, while ethical imperatives center on fairness, transparency, and accountability to prevent amplification of existing health disparities. The paper further explores workforce development needs, emphasizing AI literacy and cross-sector collaboration to enable informed use of AI insights. This viewpoint presents a novel AI Decision Support Lifecycle framework specifically designed for governmental public health emergency response, mapping six phases from problem definition through post-emergency evaluation. We provide stakeholder-specific recommendations for model developers, health agencies, and elected officials, and illustrate practical application through a detailed case example and use cases. Drawing on empirical evidence regarding digital health technologies and AI governance, we emphasize that technology deployment alone is insufficient. Successful implementation requires complementary investments in organizational capacity, data infrastructure, workforce training, community engagement, and continuous evaluation. AI integration also requires robust governance frameworks, continuous model evaluation, and alignment with existing crisis management structures. Policy recommendations highlight the importance of ethical AI frameworks, risk assessments, and public engagement to foster trust. Ultimately, AI can strengthen public health decision-making if developed and implemented responsibly within transparent and equitable systems.

Experimental psychology has long shown that task switching imposes cognitive demands and increases error rates, yet its impact in high-stakes real-world settings remains unclear. Here we provide causal evidence of switching costs in the context of organ transplantation. Leveraging quasi-random organ arrivals as a natural experiment, we analyse national registry data on 316,742 US transplants from 2007 to 2019. We find that, when surgeons switch organ types (for example, from liver to kidney) across consecutive surgeries, patients' 1-year post-transplant mortality increases by 0.66 percentage points (95% confidence interval 0.39-0.94; P < 0.001), a 14.8% increase relative to baseline. These risks can potentially be mitigated through structured scheduling, longer intervals between procedures, and greater surgeon experience. Our findings identify task switching as a modifiable risk factor in expert performance and offer potential strategies to improve outcomes in high-stakes environments.

Background Effective invigilation is crucial to the dependability of high-stakes medical exams. MBBS theory exams in the context of undergraduate medical education involve a significant number of candidates and require meticulous preparation to guarantee operational efficiency, security, and fairness. Despite the significance of invigilation, there is a dearth of empirical information on the effects of invigilation on academic integrity in the Indian setting. Materials and methods A prospective, observational, descriptive study was carried out at the Government Medical College in Nagpur during the MBBS theory exams. All registered candidates were included in the study. Structured observation and documentation formats were employed to monitor invigilation operations in real time. Candidate registration and attendance, absenteeism, academic integrity infractions (like attempts at malpractice and unauthorised use of electronic devices), operational problems (like mistakes in documentation and disruptions in procedures), medical emergencies, and the way each exam session was conducted were all included in the data that were gathered. Descriptive statistics were used to analyse the data, and the results were displayed as percentages and frequencies. Results A 98.1% attendance rate was achieved by 210 of the 214 registered candidates who took the test. Four candidates (1.9%) were affected by the only incident that was documented, i.e., absenteeism. Over the course of seven examination sessions, no instances of detected malpractice, unauthorised electronic or device breaches, paperwork errors, procedural disruptions, or medical/emergency incidents were seen. There was no need for invigilator interventions, as every session started on time and proceeded smoothly. Conclusion This study demonstrates how high attendance, seamless examination conduct, and the preservation of academic integrity during MBBS theory exams may be guaranteed by organised invigilation, adequate manpower deployment, and standardised operating standards. Exam governance and quality control in medical education may be improved by routinely recording and analysing invigilation procedures.

Self-presentation theory suggests people strategically adjust trait displays to meet evaluative goals, meaning faking can sometimes enhance the link between scores and real-world performance. We tested this in a large-scale military selection field experiment (N = 1,133) by manipulating the salience of self-presentation motives during personality assessment. We examined how varying the salience of self-presentation affects personality trait levels, convergent validity with low-stakes scores, and the ability to predict performance and career outcomes. Participants completed the same personality inventory under low, moderate, or high self-presentation. Despite trait score inflation, convergent validity with low-stakes benchmarks remained largely equivalent, and predictive validity was preserved or even enhanced under high-salience conditions. Notably, traits such as conscientiousness and extraversion showed stronger predictive utility when self-presentation motives were made explicit. These findings challenge the common view that response distortion inherently undermines test validity and instead suggest that motivated self-presentation may reflect context-relevant trait expression.

Traditional anthropometric methods for personalising equipment in high-stakes professions are often costly, time-consuming, and lack scalability. This study proposes and validates a low-cost, human-centered framework that integrates machine learning with usability evaluation to address this problem. The framework consisted of two stages: first, applying clustering algorithms to anthropometric data to establish a data-driven sizing model; and second, developing a smartphone-based prototype to validate the framework's real-world applicability. A comprehensive evaluation with 20 university students and a supplementary validation with 6 active Air Force Academy students demonstrated the framework's success, achieving an average System Usability Scale (SUS) score of 83 (82.5) and a total Questionnaire for User Interaction Satisfaction (QUIS) score of 209.55 (187.33). The data model was also validated, with key anthropometric variables effectively stratifying complex body types (p&#xa0;<&#xa0;.001). The primary contribution of this study is a generalisable framework for developing user-accepted personalised fitting systems in resource-constrained settings. This study provides a validated, low-cost framework for practitioners in military or high-stakes professions. By leveraging smartphone imaging and machine learning, organisations can rapidly develop user-accepted, data-driven sizing systems that replace outdated manual measurements to enhance personnel safety and operational effectiveness.

Occupational ApplicationThis study found that vendors were involved in nearly one-third of all observed flow disruptions during orthopedic surgery, with a disproportionate share linked to coordination issues and protocol failures, including breaches of the sterile field. While vendors provide critical technical expertise on equipment and implants, their involvement can unintentionally blur role boundaries and disrupt team coordination in high-stakes environments. For ergonomics and human factors practitioners, these findings underscore the importance of designing systems that support clearer role delineation, structured integration of non-clinical participants, and improved communication protocols in surgical teams. Practical applications include developing vendor orientation programs, establishing explicit boundaries on clinical versus technical responsibilities, and training OR staff to effectively leverage vendor expertise without over-reliance. Addressing these challenges can improve team resilience, reduce safety risks, and optimize workflow efficiency in surgical and other complex, multidisciplinary work settings. Background: Surgery demands coordination, yet flow disruption (FD) interruptions that divert attention are common and can undermine safety. Among the contributors to FDs is the surgical vendor, an external representative who provides expertise on the prosthetic device being implanted. Although vendors are valuable resources, their presence in the operating room (OR) has also been associated with safety risks.Methods: This study examines the nature and frequency of vendor-related FDs during orthopedic surgery. Trained human factors observers were embedded in orthopedic ORs and systematically documented FDs in real time. Disruptions were subsequently categorized using the RIPCHORD-TWA taxonomy and analyzed to quantify vendor involvement.Results: Of 1,387 observed FDs, vendors were involved in 425 (31%). Despite being one of several OR participants, vendors accounted for a disproportionate share of protocol-related failures, including 13 of 31 (42%) observed breaches of sterile field and other procedural deviations.Conclusion: Vendors provide essential technical knowledge while also representing a significant source of disruption. These findings highlight the need for clearer role delineation, structured integration of vendors into surgical teams, and enhanced training for both vendors and OR staff to minimize inappropriate task delegation. Addressing these issues through structured integration, role delineation, and team-centered process redesign can enhance human-system performance and occupational safety in high-stakes surgical environments.

Value-based decision-making engages brain-wide motivational, cognitive, and motor processes. Yet, information integration and gating that culminate in immediate decisions upon salient events likely occur within small neural nuclei and cortical layers at the mesoscale not resolved with conventional human neuroimaging. Using submillimeter-resolution 7&#x2009;T functional MRI with acquisition-matched anatomical references and a lottery choice task incorporating salient superhigh stakes, we dissociated mesoscale operations spanning a brainstem-prefrontal-striatal pathway during choice and outcome processing. The locus coeruleus, caudate, and prefrontal cortex showed enhanced activity during superhigh-stake choices, while the substantia nigra/ventral tegmental area and nucleus accumbens additionally distinguished gains from losses. In contrast, gray-matter bridges between caudate and putamen were associated with faster responses. Laminar analyses revealed deeper prefrontal layers predominating during choice selection and superficial layers during outcome evaluation. Here, we show a mesoscale framework integrating brainstem modulation, striatal gating, and laminar cortical computation in human decision-making upon salient events.

Policy Points Researchers investigate how recent elections in the United States have influenced mental health, especially among political- and policy-based election losers. The previous two presidential elections worsened the self-reported mental health of Americans on average. Likely partisan election losers and those who had the most to lose in terms of health policy were even more likely to have their mental health affected by the results of elections. As American politics has become increasingly polarized and the perceived stakes of elections have loomed larger in recent years, elections have become a source of worsening mental health for Americans. Politics is increasingly important to many Americans. Yet little is known about how the increasing centrality of politics affects Americans' mental health. This work aimed to evaluate how recent polarized elections have influenced Americans' mental health. To investigate this question, we compared online search interest in politically related mental health issues and self-reported mental health data. Analyses explored changes before and after election days in 2020 and 2024. The two outcome variables were aggregate Google search interest in politics-related mental health issues and individual responses to the following item from the Behavioral Risk Factor Surveillance System (BRFSS): ''Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30&#xa0;days was your mental health not good? With BRFSS, we compared differential changes for likely Democrats and Republicans using multiple proxy measures and for those with health policy interest in the election. The 2020 and 2024 presidential elections substantially increased interest in politics-related mental health issues online. The 2020 election led to just under 0.2 additional days of poor mental health (P&#xa0;<&#xa0;.05), and the 2024 election led to just under 0.5 additional days of poorer mental health (P&#xa0;<&#xa0;.05). Likely losing partisans and those who stood to lose out from Trump's reelection in terms of health policy were found to drive most of this relationship, with just under 1 full additional day of poorer mental health for each group. The stakes of elections in this polarized era of American politics are worsening the mental health of Americans. Additional resources may be necessary to allow therapists and clinicians to navigate additional care-seeking surrounding and following elections.

Informal caregivers are widely recognised as part of the 'unit of care' in palliative care, yet this recognition has rarely been translated into clearly specified ethical obligations. The present paper argues that informal caregivers are not merely instrumentally relevant to patient-centred care but are independent moral stakeholders whose vulnerability grounds direct obligations of support. The analysis demonstrates that ethical obligations towards caregivers cannot be justified solely by reference to patient welfare. While many such duties are best understood as prima facie obligations, some reach the level of threshold obligations where caregivers' fundamental interests-such as autonomy, integrity or protection from serious harm-are at stake. The paper argues that the provision of support to informal caregivers should not be regarded as a discretionary component of good palliative care but a threshold requirement of ethically grounded palliative practice, with implications for clinical decision-making and institutional responsibility.

Much of medical education focuses on the progression from novice to competent practitioner, yet less attention has been given to how senior clinicians continue to learn after core competence is established. This article argues that later stage development is less about acquiring additional knowledge and more about refining judgment, emotional regulation, perceptual sensitivity, feedback habits, and adaptability in rare or high stakes clinical situations. Drawing on literature on expertise, deliberate practice, self-calibration, and contextual performance, the article proposes that the continued growth of senior clinicians can be supported through intentional educational design. Practical strategies include stress preparation, mental rehearsal, micro skill refinement, focused coaching, structured feedback, perceptual calibration, and recovery-oriented learning cycles. The concept of the final one percent is offered as a way to understand how advanced clinicians continue to improve in precision, reliability, and adaptability across a career.

The Automated Essay Scoring (AES) systems confront two fundamental challenges: opaque "black-box" decision-making that limits educator trust, and insufficient validation across linguistically diverse educational contexts. This study proposes IRT-AESF, an innovative framework that bridges educational measurement theory and artificial intelligence by integrating item response theory (IRT) with deep learning. The framework generates three theoretically grounded psychometric parameters: student ability, item difficulty, and item discrimination, which provide transparent and interpretable explanations for scoring decisions. We rigorously evaluated IRT-AESF through 5-fold cross-validation on three large-scale datasets comprising 41,328 authentic essays from English and Chinese educational settings, including both classroom assessments and high-stakes examinations. Results demonstrate statistically significant improvements over competitive baseline models, achieving an 8.4% relative increase in quadratic weighted kappa while maintaining robust cross-lingual performance. This research advances the development of transparent, trustworthy automated assessment systems that deliver not only scores but meaningful diagnostic insights for educational practice.

LLMs are increasingly supporting decision-making across high-stakes domains, requiring critical reflection on the socio-technical factors that shape how humans and LLMs are assigned roles and interact during human-in-the-loop decision-making. This paper introduces the concept of human-LLM archetypes -- defined as re-curring socio-technical interaction patterns that structure the roles of humans and LLMs in collaborative decision-making. We describe 17 human-LLM archetypes derived from a scoping literature review and thematic analysis of 113 LLM-supported decision-making papers. Then, we evaluate these diverse archetypes across real-world clinical diagnostic cases to examine the potential effects of adopting distinct human-LLM archetypes on LLM outputs and decision outcomes. Finally, we present relevant tradeoffs and design choices across human-LLM archetypes, including decision control, social hierarchies, cognitive forcing strategies, and information requirements. Through our analysis, we show that selection of human-LLM interaction archetype can influence LLM outputs and decisions, bringing important risks and considerations for the designers of human-AI decision-making systems.