High-stakes Environment Research Articles

Causal Inference and AI: Elevating Decision-Making in Uncertain Environments

Artificial intelligence (AI) has changed the way decisions are made in industries; however, its impact may be limited in situations with uncertainty where cause and effect relationships play a crucial role. While conventional AI models are good at spotting connections between variables they may struggle when it comes to predicting results in fields such as healthcare finance and self-driving vehicles where understanding causation is key. Causal inference an emerging area in AI goes beyond finding correlations and enables systems to make stronger and dependable decisions, in intricate settings. By incorporating reasoning into AI systems operations can enhance their predictive abilities by comprehending the fundamental mechanisms governing cause and effect connections This piece delves into the amalgamation of causal deduction in AI by examining its theoretical groundwork as well as practical implementations and the obstacles and advantages of merging causal understanding with machine learning. Through real life examples and new methodologies such as networks and counterfactual logic establishment this paper underscores how causal inference can enhance AIs decision making skills by rendering them more flexible and efficient, in ambiguous scenarios. Keywords: Artificial Intelligence, Causal Inference, Machine Learning, Decision-Making, Bayesian Networks, Counterfactual Reasoning, Uncertainty, High-Stakes Environments, Predictive Models, Causality.

Unveiling the experiences of operation room nurses on ethical issues: a phenomenological study in Adama Hospital Medical College, Ethiopia, 2022

BackgroundOperating room nurses are critical in upholding high ethical standards in fast-paced and high-stakes environment to guarantee the safety and well-being of patients undergoing surgery to resolve life-threatening situations. A deeper understanding of ethical issues in the Operating Room helps tailor interventions and policies to address real-world challenges, enhance patient care, and assist healthcare professionals.ObjectiveThis study explored the lived experiences of Operating Room nurses regarding ethical issues in Operating Room at Adama Hospital Medical College in Adama, Ethiopia, in 2022.MethodA qualitative study using the hermeneutic phenomenological method was conducted at Adama Hospital Medical College from March 15, 2022 to April 15, 2022. Eighteen nurses were selected as participants using maximum variation purposive sampling. The data was collected through semi-structured in-depth interviews. The data was then imported into Atlas Ti 8 software to be coded and categorized. To ensure trustworthiness, the basic principles such as credibility, confirmability, dependability, and transferability were applied. Data analysis was conducted based on the Van Manen (2016) method and the results were summarized using thematic analysis.ResultsThe study revealed two main themes and ten sub-themes regarding ethical issues faced by operating room nurses. The main themes were issues related with safeguarding patient dignity and issues of Staff Professionalism and Respectful Interaction.ConclusionUpholding ethical principles and standards are critical for improving patient care and fostering positive relationships. Concepts such as Preoperative communication, ethical considerations in emergency surgeries, privacy, cultural and gender sensitivity, staff rapport, personal and professional competence, bearing responsibility, caring for patients with justice, and sterilization standards and aseptic techniques are crucial for participants. Addressing ethical concerns and ensuring compliance will enhance healthcare delivery in perioperative settings.

Toward Transparent and Controllable Quantum Generative Models.

Quantum generative models have shown promise in fields such as quantum chemistry, materials science, and optimization. However, their practical utility is hindered by a significant challenge: the lack of interpretability. In this work, we introduce model inversion to enhance both the interpretability and controllability of quantum generative models. Model inversion allows for tracing generated quantum states back to their latent variables, revealing the relationship between input parameters and generated outputs. We apply this method to models generating ground states for Hamiltonians, such as the transverse-field Ising model (TFIM) and generalized cluster Hamiltonians, achieving interpretability control without retraining the model. Experimental results demonstrate that our approach can accurately guide the generated quantum states across different quantum phases. This framework bridges the gap between theoretical models and practical applications by providing transparency and fine-tuning capabilities, particularly in high-stakes environments like drug discovery and material design.

The Making of Confident Surgeons: Why and How?

Low self-confidence in surgical residents can be associated with poor self-efficacy and perceptions of sub-optimal preparedness for practice at graduation. The influence of social and biologic determinants of confidence deserves further study. Through a randomized controlled trial of procedure-specific didactic and low fidelity simulation training for vaginal surgery, we showed positive correlations between self-confidence and objective performance in the real operating room for three different surgical procedures and through validated scales. This demonstrates an accurate ability of novice surgeons to self-monitor in a high-stakes environment. Our trial results (described in full elsewhere), combined with our multiple one-on-one teaching interactions with surgical trainees through the trial, incentivized us to evaluate self-confidence in view of optimizing it through directed training and feedback. The current opinion piece summarizes our main findings for surgical educators and emphasizes their role engaging with trainees at extremes of confidence.

AI and Machine Learning Solutions for Real-Time Fault-Tolerant System Design in Critical Applications

This study presents an innovative framework for implementing real-time fault-tolerant systems using artificial intelligence (AI) and machine learning (ML) to enhance reliability and resilience in critical applications. Addressing the needs of sectors such as aerospace, healthcare, automotive, and industrial automation, the proposed system integrates fault detection, isolation, and recovery mechanisms into a multi-layered architecture. Through the use of deep learning for accurate anomaly detection and reinforcement learning for rapid fault isolation, the system achieves high fault tolerance with minimal latency. The framework leverages edge computing for real-time data processing, ensuring timely responses to faults without excessive computational demands. Results from multiple case studies demonstrate significant improvements in fault detection accuracy, isolation speed, and recovery rates, affirming the framework’s adaptability and effectiveness in high-stakes environments. These findings highlight the potential of AI-driven fault-tolerant systems to elevate operational safety and reliability standards across diverse critical industries.

Comparative Sensitivity Analysis in Composite Material Selection: Evaluating OAT and COMSAM Methods in Multi-criteria Decision-making

The decision-making process is critical across various fields, influencing essential aspects like performance, safety, and sustainability. In engineering and materials science, selecting suitable materials is a complex process involving multiple interdependent criteria, which impact the reliability and lifecycle of a design. Traditional sensitivity analysis methods, such as the one-at-a-time (OAT) approach, assess the effects of individual parameter changes but often fail to capture the cumulative impact of simultaneous modifications. It can limit the effectiveness of decision tools in real-world scenarios where multiple factors vary concurrently. This study utilizes the Comprehensive Sensitivity Analysis Method (COMSAM) to address limitations in traditional sensitivity analysis by modeling simultaneous parameter adjustments across multiple criteria. Specifically, the study aims to validate COMSAM’s effectiveness in the practical problem of composite material selection, comparing it with the traditional OAT approach within a multi-criteria decision-making (MCDM) framework. By integrating both sensitivity analysis techniques with the Characteristic Objects Method (COMET), this research ensures robust evaluation and mitigates vulnerabilities like the rank reversal paradox. The findings demonstrate that COMSAM provides enhanced insights into the impact of parameter interdependencies, offering a more resilient foundation for decision-making in high-stakes environments where material properties and performance are paramount.

Artificial Intelligence and Artificial Stupidity: The Inseparables

Artificial intelligence (AI) has long been heralded for its ability to simulate human intelligence, enabling machines to perform complex tasks such as decision-making, problem-solving, and data analysis. However, alongside the advancements in AI, the concept of artificial stupidity (AS) has gained attention. AS refers to the limitations and errors made by AI systems, often resulting from incomplete data, biased algorithms, or the inherent restrictions placed on AI to simulate more human-like decision-making. These instances of "stupidity" can lead to nonsensical or harmful outcomes, especially when AI is applied to critical areas such as healthcare, autonomous systems, and legal decision-making. This narrative review explores the duality between AI's potential and its flaws, emphasizing the importance of understanding both AI and AS in developing robust, safe, and ethical AI applications. By addressing the causes of artificial stupidity, such as algorithmic limitations and poor data quality, researchers and developers can improve the reliability and decision-making capabilities of AI systems. There is also the need for human oversight and ethical considerations to mitigate the negative impacts of artificial stupidity, especially in high-stakes environments.

Ideal Workers, Supporting Actors, or Thrill Seekers? How Coworker Demands Influence Ambulance Volunteers’ Experiences of Freedom and Meaningful Work

AbstractFor nonprofit organizations (NPOs) struggling to attract adequate numbers of volunteers, examining what makes nonprofit engagement meaningful is essential because disenchanted volunteers can simply quit. Yet, the assumption that freedom is a core aspect of the volunteer experience and of meaningful work may not hold true in high-stakes environments where volunteers must demonstrate high levels of commitment and expertise. This study aims to analyze how freedom plays out in high-stakes volunteering and its impact on meaningful work. Drawing on interviews with volunteer and paid ambulance crew working in nine stations in Aotearoa New Zealand, the study explores how “super-volunteers” talk about freedom in the context of their on-road work and how coworkers communicatively attempt to influence volunteers’ freedom. Three volunteer profiles emerged from the analysis: ideal workers, supporting actors, and thrill seekers. Most paid staff encouraged ideal workers to strive for self-realization, a form of positive freedom in work, which led to optimal clinical performance. Supporting actors privileged self-determination or positive freedom at work, although coworkers successfully pushed them to contribute to basic emergency work. Because thrill seekers demanded freedom from boring or dirty jobs, appeals to teamwork failed to sway them. The study makes two key contributions. First, the diversity of freedoms volunteers evoked and resisted underscores the importance of nuancing the assertion that volunteering is a “free” act. Second, although the meaningful work literature is drifting in the pro-freedom direction, it shows that the freedoms enacted by volunteers or promoted by coworkers were arguably “mistaken”—for volunteers, patients, and the NPO itself.

An Experimental Study to Explore the Effectiveness of a 3D Maritime Serious Game Prototype: Maritime Leaders at Sea

This paper conducts a comprehensive experimental study designed to investigate the effectiveness of a maritime serious game called Maritime Leaders at Sea (ML@S). The 3D cutting-edge prototype targets to enhance leadership and teamwork skills of undergraduate maritime students via engaging them in immersive gameplay scenarios reflective of real-world challenges at sea. Competing the ML@S sessions, the number of 76 students were included in the feedback assessment survey to assess their perceptions and attitudes on the simulated environment. The feedback is also linked to the learning analytics perspective to involve time and scoring data of players through different levels at operational scenarios. Preliminary analyses reveal promising results, suggesting a positive correlation between participation in ML@S, feedbacks after sessions, and improvements in leadership and teamwork as key competencies. It is anticipated that this study will make significant contributions towards game developers, technology start-ups, educational institutions, and global shipping companies. The implications of the study offer valuable insights for the development of future serious games aimed at enhancing essential skills of maritime human resources in dynamic and high-stakes environments.

Emergency management decision-making in a changing world: 3 key challenges

Managing emergencies is taxing for individuals due to the stress of making decisions in dangerous, high-stakes and time-constrained environments. These complex, dynamic environments also make it difficult to coordinate as other responders perform different roles that may have conflicting goals. This study explored some of the challenges faced by emergency management decision-makers through a literature review of 70 papers identified from SCOPUS and EBSCO database searches. Three major challenges for emergency management were identified: stress and fatigue, interoperability and ethical decision-making. Each of these challenges is examined to explore their nature and how they are likely to evolve in the future. This paper provides helpful advice on how to mitigate these challenges. We argue that to better meet these challenges, emergency services organisations need to develop and maintain appropriate doctrine and training, develop a supportive organisational culture and effectively learn the lessons of previous critical incidents.

Explainable AI (XAI) in healthcare: Enhancing trust and transparency in critical decision-making

The integration of artificial intelligence (AI) in healthcare is revolutionizing diagnostic and treatment procedures, offering unprecedented accuracy and efficiency. However, the opacity of many advanced AI models, often described as "black boxes," creates challenges in adoption due to concerns around trust, transparency, and interpretability, particularly in high-stakes environments like healthcare. Explainable AI (XAI) addresses these concerns by providing a framework that not only achieves high performance but also offers insight into how decisions are made. This research explores the application of XAI techniques in healthcare, focusing on critical areas such as disease diagnostics, predictive analytics, and personalized treatment recommendations. The study will analyse various XAI methods, including model-agnostic approaches (LIME, SHAP), interpretable deep learning models, and domain-specific applications of XAI. It also evaluates the ethical implications, such as accountability and bias mitigation, and how XAI can foster collaboration between clinicians and AI systems. Ultimately, the goal is to create AI systems that are both powerful and trustworthy, promoting broader adoption in the healthcare sector while ensuring ethical and safe outcomes for patients.

Leading Through Noise: Operating Room Noise Challenges for Staff and Leadership Techniques to Ensure Optimal Operational Performance.

Noise and distractions in the operating room (OR) critically impact surgical performance and patient outcomes, particularly in high-stakes environments such as trauma surgery. While historical hospital environments prioritized quiet to facilitate recovery and reduce stress, contemporary ORs, especially those handling trauma cases, face increasing noise challenges due to advanced surgical instruments, alarms, and staff conversations, often surpassing federal exposure limits. This review investigates OR noise sources, including staff activities and equipment, analyzing their effects on cognitive load, communication, and error rates among healthcare workers. It identifies high-risk scenarios and vulnerable groups, highlighting the necessity for targeted interventions. Key strategies include implementing strict noise control policies, using noise-reducing materials in OR design, and educating staff on noise impacts. Additionally, structured communication protocols and continuous monitoring systems are advocated to enhance operational efficiency and safety. Surgeon leadership is pivotal in balancing assertiveness and empathy to maintain a productive team dynamic. Furthermore, surgeons significantly boost OR efficiency and safety by adopting these protocols, promoting inclusive team dynamics, and applying noise-reduction strategies. These practices safeguard patient care and foster a more collaborative work atmosphere, aligning all team efforts toward optimal patient outcomes. This holistic approach emphasizes the need for continuous improvement and adaptability in surgical practices to meet modern healthcare demands, particularly in trauma surgery's fast-paced, unpredictable realm. Collectively, these measures can enhance patient safety and improve conditions for surgical teams, providing a framework for quieter, more focused OR environments that ultimately elevate surgical outcomes and healthcare quality.

Certifying Generative AI: Retrieval-Augmented Generation Chatbots in High-Stakes Environments

Certifying Generative AI: Retrieval-Augmented Generation Chatbots in High-Stakes Environments

On Meter and the Social Dynamics of Cueing in Bill Monroe’s “Muleskinner Blues”

“Muleskinner Blues,” the signature song of bluegrass patriarch Bill Monroe, is at once central to the bluegrass canon and yet metrically enigmatic, featuring a flexible timing structure that fluctuated wildly between performances. This article engages in a longitudinal study of 165 performances of “Muleskinner Blues” across Bill Monroe’s career to explore how the musicians that rotated through his band, the Blue Grass Boys, cognitively grappled with the song’s flexible structure. Through a series of analytical vignettes, I will detail the distributed cognitive system that drives performances of “Muleskinner Blues,” giving special attention to musical cues as tools for calling collective attention to structurally important moments of action. Additionally, I show how the song’s flexible meter was weaponized by Monroe in acts of musical hazing, antagonizing his musicians in the high-stakes environment of a live performance. Through these analyses, I show how the song’s peculiar meter centered Monroe musically, structurally, and socially, transforming “Muleskinner Blues” into a potent vehicle for the masculine ideology of the father of bluegrass music.

Evaluating performance: a scoping review on video-based assessments of non-technical skills in the operating room

Background: Patient safety remains a critical concern in the high-stakes environment of the operating room (OR). Human factors and non-technical skills (NTS) play pivotal roles in surgical performance and in preventing errors. Various assessment tools and methodologies have been developed to evaluate NTS among OR personnel, reflecting the growing recognition of their importance. Objectives: To map and analyze NTS among OR personnel during real-life surgeries using medical video recording systems and to evaluate NTS assessment tools described in existing literature. Methods: Four databases, PubMed, Web of Science, Medline and Embase, were searched for relevant studies. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol was followed. Eligibility criteria included studies focusing on video recordings providing an overview of the entire OR with a focus on NTS. Results: Twenty-four articles were included in this scoping review. A total of eight NTS were assessed, most frequently communication (79%), leadership (54%), teamwork (50%) and situational awareness (50%). Observational methods varied, ranging from one or more cameras to more sophisticated medical data recorders, such as the OR Black Box (ORBB). Additionally, a wide variety of assessment methods were used, mostly validated tools (NOTSS, SPLINTS, ANTS), but also broader systems-based frameworks (SEIPS) or self-developed tools. Conclusions: This scoping review highlights the diverse methodologies used to measure NTS in the OR using medical video recordings, indicating a need for further research to address challenges and standardize practices. Despite the proven potential benefits of video recording in the OR, legal, ethical and logistical challenges may serve as possible barriers to actual implementation.

Evaluating the Effectiveness of Penalty Kicks in the UEFA Champions League: Optimal Techniques and Directions for Scoring Success

This study examined the strategic execution of penalty kicks in professional soccer, focusing on the high-stakes environment of the UEFA Champions League. Utilizing a dataset of 243 penalty kicks from five seasons (2018/2019–2022/2023), sourced from YouTube and cross-referenced with Transfermarkt, we employed a detailed nine-zone goal classification system to analyze shot distribution and success rates. We investigated various factors influencing penalty outcomes, including match stage, home-field advantage, minute of the match, current score, player status, previous score, footedness, and rebound goals. Findings indicated that shots aimed at the center and upper areas of the goal have the highest success rates. It was also found that home advantage considerably enhances scoring chances in both group and knockout stages. This research fills a gap in the literature by providing insights into penalty kick strategies in the UEFA Champions League, offering valuable implications for coaching practices, player training, and analytical frameworks in high-pressure scenarios.

Cognitive Task Analysis: Eliciting Expert Cognition in Context

Cognitive Task Analysis (CTA) is a powerful methodological approach that can enhance the rigorous elicitation and documentation of complex cognitive processes within interview-based qualitative research. We provide insights into this set of semi-structured interviewing techniques that we contend have much to offer management researchers who wish to understand the complexities of expert cognition within specific work-related tasks. Distinct from traditional semi-structured interview methods, CTA is designed to identify the knowledge requirements underpinning expertise in complex work domains. First, we present CTA as a robust approach to eliciting complex cognition and note why, when, and where management scholars might best use its techniques. Second, we provide two examples of how CTA methods have been used to research management; specifically, using the Critical Decision Method to explore management in high-stakes environments, and Applied Cognitive Task Analysis to explore global leadership. In ending, we propose greater use of this pragmatic approach in management research and highlight potential avenues for future research that will advance understanding of complex cognition at work.

A many-facet Rasch measurement model approach to investigating objective structured clinical examination item parameter drift.

Objective Structured Clinical Examinations (OSCEs) are widely used for assessing clinical competence, especially in high-stakes environments such as medical licensure. However, the reuse of OSCE cases across multiple administrations raises concerns about parameter stability, known as item parameter drift (IPD). AIMS &OBJECTIVES: This study aims to investigate IPD in reused OSCE cases while accounting for examiner scoring effects using a Many-facet Rasch Measurement (MFRM) model. Data from 12 OSCE cases, reused over seven administrations of the Internationally Educated Nurse Competency Assessment Program (IENCAP), were analyzed using the MFRM model. Each case was treated as an item, and examiner scoring effects were accounted for in the analysis. The results indicated that despite accounting for examiner effects, all cases exhibited some level of IPD, with an average absolute IPD of 0.21 logits. Three cases showed positive directional trends. IPD significantly affected score decisions in 1.19% of estimates, at an invariance violation of 0.58 logits. These findings suggest that while OSCE cases demonstrate sufficient stability for reuse, continuous monitoring is essential to ensure the accuracy of score interpretations and decisions. The study provides an objective threshold for detecting concerning levels of IPD and underscores the importance of addressing examiner scoring effects in OSCE assessments. The MFRM model offers a robust framework for tracking and mitigating IPD, contributing to the validity and reliability of OSCEs in evaluating clinical competence.

Research on the Design of Aviation and Aerospace Hatch Door Mechanisms and Their Future Bionic Prospects

The design of the space hatch door mechanisms is crucial in the aerospace field, impacting not only durability and reliability but also the life safety of astronauts during space missions. This review extensively researches vehicle doors and hatches in civil and military systems across various environments, including land, sea, deep sea, aviation, aerospace, and extreme conditions. Specially, it focuses on the structural design of hatches and related mechanisms in civil aviation and military aerospace environments, such as opening and closing mechanisms, release mechanisms, locking mechanisms, sealing mechanisms, and the ergonomic design of door structures. The review highlights the integration of bionic design principles with hatch mechanisms to explore future solutions. By systematically examining these aeras, this review addresses the lack of comprehensive studies in previous reviews, which often overlook the interconnectivity and applicability of hatch mechanisms across different fields. The absence of such holistic reviews has led to fragmented knowledge and missed opportunities for cross-industry innovation. This review aims to fill these gaps by providing a wide range of design solutions and offering insights that can enhance the development of more reliable, efficient, and safe hatch mechanisms in aerospace and other high-stakes environments.

Advancing strategic procurement: Enhancing efficiency and cost management in high-stakes environments

Advancing strategic procurement: Enhancing efficiency and cost management in high-stakes environments