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HIGHLIGHTS: PIVCs often cause pain, irritation, or infection. Regular and careful catheter checks can decrease complications and improve patient outcomes. Implementation of the I-DECIDED® tool led to fewer idle catheters and complications. We present ideas for implementing the tool and how to overcome some common barriers. Introduction: Peripheral intravenous catheter (PIVC) assessment and decision making should be evidence based to minimize risks and enhance patient care. Exploring implementation strategies from successful outcome studies can raise health care professionals' awareness, boost adherence to evidence-based protocols, and elevate PIVC care standards. Aim: To describe the implementation of the I-DECIDED® device assessment and decision tool for PIVC management in the Australian and Brazilian contexts. Methods: The Promoting Action on Research Implementation in Health Services (PARIHS) framework (evidence, context, facilitation) was used to prospectively plan the implementation of the tool in adult inpatient units in Australia and to retrospectively analyze the implementation of the tool in a pediatric inpatient unit in Brazil. Similarities and differences in the implementation studies were explored. Results: Implementation of the tool in Australia and Brazil demonstrated that prevention and early detection of PIVC complications is achievable with standardized assessment and decision prompts. Both contexts witnessed a reduction in idle PIVCs, decreased insertion site complications, improved dressing quality, and significant improvements in documentation following implementation. Common implementation strategies included champions, education, badge cards, and posters. Enabling flexibility and context-specific education strategies was essential. Conclusion: Standardization of PIVC assessment and decision making using the I-DECIDED® tool reduced the prevalence of idle catheters and complications in Australia and Brazil. Implementation of the tool in different contexts confirms its utility and relevance for PIVC management in diverse health care settings. Promotion and adoption of the tool as a PIVC management bundle could increase patient safety and reduce health care costs. [ABSTRACT FROM AUTHOR]

This article provides a scoping review of 25 years of research on the notion of cognitive authority (CA), examining its conceptualization and empirical examination. The review follows the PRISMA statement and its extension for scoping reviews. Peer‐reviewed journal articles on CA were identified through database searching with the specific search term "cognitive authorit*" in the title or abstract and covering work published in 2022 or earlier. In total, 235 unique references were identified, and their abstracts and then selected full texts were screened according to predetermined exclusion criteria. In total, 40 articles were included in the review, extracted, and analyzed with qualitative content analysis focusing on the conceptualization of CA, the methodological approach taken to examine it, and the different spheres of knowledge and levels of activity the research addressed. Based on this analysis, four parallel lines of research were identified including studies conceptualizing CA: (1) as an indicator of information source quality, (2) as discursively constructed, (3) as situated in social mechanisms and settings, and (4) as institutional legitimacy of science and professions. This body of research has extended Wilson's (1983; Second‐hand knowledge: An inquiry into cognitive authority. Greenwood Press) original work contributing to our understanding of CA at individual, communal, and societal levels. [ABSTRACT FROM AUTHOR]

Sense‐making, sensemaking, and sense making are terms used in different disciplines. Similarities of usage seem unclear. (1) to examine the concepts used in different approaches to sense‐making/sensemaking/sense making; (2) to identify, classify and synthesize recent studies relevant to information science, as well as similar group on sensemaking in education research; (3) to reflect on future directions for sense‐making/sensemaking methodology in information science. The objectives were to retrieve, examine, classify and perform meta‐synthesis on sense‐making/sensemaking studies in both information science and education research. The review used systematic review principles, with selection criteria for case studies for examination in both information science and education sets. The final meta‐synthesis used a meta‐ethnographic approach, together with findings of recent overviews on organizational sensemaking, and other information science reviews. Qualitative sense‐making studies in information science often used Dervin's SMM (sense‐making methodology) and studies in organizations and education frequently used Weick's organizational sensemaking. Different mixed methods approaches were identified. Sense‐making is actively used in research and practice in information science and knowledge management. Using a coherent sense‐making methodology helps and dialogic principles are useful in planning, data collection and analysis. Individual and collective sense‐making are important to information science. [ABSTRACT FROM AUTHOR]

Abstract: Wilson, PJ, Roe, G, and Kiely, J. How do they decide? Performance support staff perceptions of decision-making in elite-sport environments. J Strength Cond Res 39(2): 192-199, 2025-Decision-making is a critical component of performance support staff responsibilities, yet research exploring staff perceptions relating to key facets of decision-making is lacking. This study aimed to explore perceptions of performance support staff relating to; the importance of decision-making in their role, the value of team decision-making, strategies to enhance decision-making skills, monitoring the process and outcome of decisions, and the obstacles to good decision-making within their professional contexts. A mixed-methods approached was used to explore perceptions of decision-making in 191 performance support staff (male staff = 179, female staff = 12) working in professional ( n = 147) or international ( n = 44) sport. Performance support staff reported decision-making as extremely important to their role; however, only 31% had received formal decision-making education and revealed underdeveloped decision-making capabilities within their organizations as a barrier to good decision-making. Most performance support staff conveyed that team decision-making is a valuable endeavor and augments decision outcomes; yet articulated dysfunctional team dynamics as an obstacle to good decision-making in their environments. Largely, performance support staff reported an absence of organizational strategy and workflow systems and processes for tracking and making decisions, respectively, whereas only 22% had a consistent reviewing and updating process. When making decisions, performance support staff faced several different cognitive biases and were challenged by the environmental pressures . The perspectives documented here suggest that performance support staff would likely benefit from undertaking evidence-based training and implementing scientifically informed strategies to enhance decision-making processes within their organizations., (Copyright © 2024 National Strength and Conditioning Association.)

This article discusses the value cycle of knowledge in management education, encompassing generation, synthesis, education, and application, which ensures that research findings are both credible and usable. It suggests that journals like Annals and AMLE focus on synthesizing knowledge and advancing pedagogy, but AMP translates research into practical insights for managers and policymakers. The article also stresses that effective scholarship requires coordination across these phases, bridging the gap between theory and practice to enhance organizational decision-making. By recognizing the interdependence of these stages, scholars can maximize impact and ensure the continued relevance of management research.

Measurement-based care (MBC) research and practice, including clinical workflows and systems to support MBC, are grounded in adult-serving mental health systems. MBC research evidence is building in child and adolescent services, but MBC practice is inherently more complex due to identified client age, the family system and the need to involve multiple reporters. This paper seeks to address a gap in the literature by providing practical guidance for youth-serving clinicians implementing MBC with children and their families. We focus on MBC as a data-informed, client-centered communication process, and present three key strategies to enhance usual care child and adolescent psychotherapy via developmentally-appropriate MBC. These strategies include (1) go beyond standardized measures; (2) lean into discrepancies; and (3) get curious together. Case-based examples drawn from various child-serving settings illustrate these key strategies of MBC in child psychotherapy.

The advent of Generative AI technologies, such as ChatGPT, in November 2022, necessitated immediate and critical attention from the educational research community. The impact of GenAI in education, though not yet clear, has the potential to be transformative. More specifically, the focus of this paper is on how to integrate GenAI into elementary literacy education. We, as teacher educators, aim to showcase how to prompt ChatGPT to generate high-quality questions during a read-aloud. We discuss the easy access teachers have to GenAI tools and stress the pivotal role they have in decision-making. We encourage teachers to explore, learn, and understand how to work with GenAI tools to get the most out of it and thus facilitate their agency, teaching, and learning.

We consider the problem of collecting and processing waste material. At a production facility, a known amount of inventory is required for production (e.g., paper) for every period. Instead of new material, the facility relies on collected and processed waste material (e.g., paper waste). This material is collected from regional waste collection locations. The amount of waste material per location is uncertain, as is the quality of the collected waste, i.e., the resulting inventory when processing the material. If the inventory is insufficient at the end of a period, costly new material must be bought. Each period, decisions are made about how much waste material to collect from which location and how to route the collection vehicles accordingly. Ideally, inventory is built to hedge against quality uncertainty and to ensure efficient routing operations in future periods. We propose a stochastic lookahead method that samples a set of scenarios and solves a simplified two-stage stochastic program in every period. We show the value of our method for two case studies, one based on real-world data from Sachsen-Anhalt, Germany, and one from the literature with data from the United Kingdom. We further conduct a detailed analysis of our method and the problem characteristics. The results show that our method effectively anticipates all sources of uncertainty, reducing cost significantly compared to benchmark policies. This superior performance is due to appropriate state-dependent supplier selection that considers the percentage of material loss, available material, and routing cost for current and future periods. [ABSTRACT FROM AUTHOR]

Purpose: While being integrated together conceptually and practically, the literature on game theory in the context of financial markets lacks a cohesive understanding. This study aims to systematically scrutinize and analyse the literature of game theory in the context of financial markets, through a systematic literature review. Design/methodology/approach: A systematic literature-based approach, through the theories, context, characteristics and methodology (TCCM) framework has been applied to 97 articles, extracted and filtered from two databases, Scopus and Web of Science, for a comprehensive review and understanding of the intellectual development in the domain of game theory and financial markets. Findings: The review highlighted the most utilitarian theories within the literature, the context of research in terms of countries and industries, four themes which characterize the literature, as well as the methodologies and research designs used in this research domain. The paper also uncovered certain essential areas that present scope for further research. Research limitations/implications: While two of the largest indexation databases have been used, some relevant articles may have been excluded due to the restriction of databases and screening criteria, which may lead to the less exploration of several facets of the domain. Practical implications: Practically, the paper has implications for multiple stakeholders including traders, businesses and governments. For traders, this paper acts as a guide to entering and understanding the dynamics of financial markets. The review also covers decision-making from the perspective of firms, including venture capitalists. This paper would allow firms to understand how game theory can help influence or analyze the strategic interactions between their stakeholders in terms of information disclosure, or consumers and their behavior to stimuli from the firm's actions. Originality/value: To the best of the authors' knowledge, this study is the first of its kind that attempts to comprehensively provide an overview of the literature on game theory in financial markets. In doing so, this study shall help assess the current state of knowledge in the said field and locate gaps in the literature to propose new research directions. [ABSTRACT FROM AUTHOR]

Background Severe mental illness (SMI) incorporates schizophrenia, bipolar disorder, non- organic psychosis, personality disorder or any other severe and enduring mental health illness. Medication, particularly antipsychotics and mood stabilisers are the main treatment options. Medication optimisation is a hallmark of medication safety, characterised by the use of collaborative, person-centred approaches. There is very little published research describing medication optimisation with people living with SMI. Objective Published literature and two stakeholder groups were employed to answer: What works for whom and in what circumstances to optimise medication use with people living with SMI in the community? Methods A five-stage realist review was co-conducted with a lived experience group of individuals living with SMI and a practitioner group caring for individuals with SMI. An initial programme theory was developed. A formal literature search was conducted across eight bibliographic databases, and literature were screened for relevance to programme theory refinement. In total 60 papers contributed to the review. 42 papers were from the original database search with 18 papers identified from additional database searches and citation searches conducted based on stakeholder recommendations. Results Our programme theory represents a continuum from a service user's initial diagnosis of SMI to therapeutic alliance development with practitioners, followed by mutual exchange of information, shared decision-making and medication optimisation. Accompanying the programme theory are 11 context-mechanism-outcome configurations that propose evidence-informed contextual factors and mechanisms that either facilitate or impede medication optimisation. Two mid-range theories highlighted in this review are supported decision-making and trust formation. Conclusions Supported decision-making and trust are foundational to overcoming stigma and establishing 'safety' and comfort between service users and practitioners. Avenues for future research include the influence of stigma and equity across cultural and ethnic groups with individuals with SMI; and use of trained supports, such as peer support workers. [ABSTRACT FROM AUTHOR]

The 2024 John Bates Clark Medal of the American Economic Association was awarded to Philipp Strack, Professor of Economics at Yale University, for his pathbreaking contributions to the study of individual decision making, which have introduced new techniques, improved our understanding of important economic phenomena, and helped spark a new wave of research on the economics of information while building bridges between modern economic theory and a wide range of adjacent disciplines. This article summarizes some of Philipp's papers, and explains how they build on and improve previous work. [ABSTRACT FROM AUTHOR]

To improve the safety of ramp vehicles changing lane and to shorten the merging distance, this paper explores the dynamic game interaction properties of vehicles merging and the consistency of vehicles' decision-making behaviors at the macro-microscopic levels. Using the exiD dataset and evolutionary game theory, the merging behavior of ramp vehicles is modeled to explore the effects of different driving states on the evolutionary convergence of strategies. Based on the game cost theory, the lane choice behavior of mainline vehicles is modeled. Validated by SUMO software, the results show that the model in this paper can significantly improve the safety of vehicle merging and reduce the merging distance. The mainline vehicles are more inclined to change lane and cut out in advance when facing the ramp vehicles under the influence of the change of advantage in the subsequent game. [ABSTRACT FROM AUTHOR]

This paper studies a key element of discrimination, namely, when stereotypes translate into discriminatory actions. Using a hiring experiment, we rule out taste-based discrimination by design and test for the presence of two types of belief-based gender discrimination. We document evidence of explicit discriminators—individuals who are willing to discriminate even when their hiring choices are highly revealing of their gender-biased beliefs. Crucially, we also identify implicit discriminators—individuals who do not discriminate against women when taking a discriminatory action is highly revealing of their biased beliefs, but do discriminate against women when their biased motive is obscured. Our analysis highlights the central role played by features of the choice environment in determining whether and how discrimination will manifest. We conclude by discussing the implications for policy design. This paper was accepted by Marie Claire Villeval, behavioral economics and decision analysis. Funding: K. Barron and S. Schweighofer-Kodritsch gratefully acknowledge financial support from the Deutsche Forschungsgemeinschaft through CRC TRR 190 [Grant 280092119]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01229. [ABSTRACT FROM AUTHOR]

The border line between Bulgaria and Romania was established a century ago on the Danube River's thalweg, going around islands. Over time, islands migrated; to avoid conflicts, islands located on the border line are declared neutral territory by both countries and they exit from use. In this context, the paper aims to draw conclusions on the spatial dynamics of the Danube River's sandy islands along the Bulgaria-Romania border. This approach is mostly a GIS work on various documents over the last century, with interpretations specific to fluvial geomorphology. Our paper showed patterns of evolution of islands. (i) The trajectory of half of the islands can be reconstituted over the last century (i.e., merging with each other and/or migrated). The trajectory of the other half of the islands can be redrawn over the last half of the century. The form and dimensions of islands did not statistically change at any time scale. (ii) At the scale of the last decades, we noticed the slow formation of bars by lee deposition due to sediment excess and then migration of alluvial bars until merging with an island or with the bank, followed by stabilization due to vegetation recruitment. (iii) Islands and their bars migrated on the countries' border line, but their evolution is slow in time. Our paper invites national authorities to periodically inventory the geomorphological trajectory of islands and bars, but to transfer the decision-making process for stable islands at local scale in order to put them into green use with benefits for local communities. [ABSTRACT FROM AUTHOR]

Purpose: The complexity of property valuation, coupled with valuers' cognitive limitations, makes some degree of error inevitable in valuations. However, given the crucial role that valuations play in the efficient functioning of the economy, there is a need for continuous improvement in the reliability of reported values by enhancing the quality of the decision-making process. The purpose of this paper is to review previous research on valuation decision-making, with particular interest in examining the approaches to improving the quality of valuation decisions and identifying potential areas for further research. Design/methodology/approach: The paper adopts a narrative approach to review 42 research articles that were obtained from Scopus and Web of Science databases and through author citation searches. Findings: Our findings show that existing literature is skewed towards examining the use of technology in the form of decision support systems (DSS), with limited research attention on non-technological (i.e. behavioural) approaches to improving the quality of valuation decisions. We summarise the non-technological approaches and note that much of the discussions on these approaches often appear as recommendations arising from other studies rather than original investigations in their own rights. Practical implications: We conclude that studies investigating the effectiveness of the non-technological approaches to improving valuation decision-making are lacking, providing various avenues for further research. Originality/value: This paper presents the first attempt to provide a comprehensive overview of non-technological approaches to improving the quality of valuation decisions. [ABSTRACT FROM AUTHOR]

This paper presents a study on the MCDM (multiple criteria decision making) hybridization methods. Three MCDM methods are used in the hybridization process: PSI (Preference Selection Index), RAPS (Ranking Alternatives by Perimeter Similarity), and MCRAT (Multiple Criteria Ranking by Alternative Trace). PSI has long been known as an MCDM method, but it has an unexplored utility: the ability to self-calculate the weights for the criteria. Because of this, PSI is also known as an MCDM method that does not require users to calculate the weights for the criteria. RAPS and MCRAT are two MCDM methods that require users to calculate the weights for the criteria. The hybridization of PSI with RAPS and MCRAT aims to use the criteria weights calculated by the PSI method to serve the ranking of alternatives using the RAPS and MCRAT methods. This hybrid model of the three methods is named the PSI-RAPS-MCRAT hybrid model. Thus, applying the PSI-RAPS-MCRAT hybrid model to rank alternatives will generate three sets of ranking results: one from using PSI, one from using PSI-RAPS, and one from using PSI-MCRAT. The accuracy of the PSI-RAPS-MCRAT hybrid model was tested through a specific problem of ranking welding robot types. The number of welding robots to be ranked is seven, while the number of criteria to reflect each type of robot is six. Spearman's rank correlation coefficient was used to evaluate the accuracy of the proposed model. The lowest being between the PSI method and the PSI-RAPS method at 0.8214, and the highest being between the PSI-RAPS method and the PSI-MCRAT method at 0.9643. The results show that the PSI-RAPS-MCRAT hybrid model has high accuracy. This study has provided a completely new and accurate method in the field of MCDM. [ABSTRACT FROM AUTHOR]

With the rapid development of the electric vehicle (EV) industry, various issues, such as the suboptimal deployment of charging facilities and inadequate distribution, hinder improvements in user satisfaction with EVs. Moreover, the interests of multiple stakeholders, including power grid companies and transportation departments, are not sufficiently addressed. To tackle these challenges, this paper proposes a comprehensive evaluation method for EV charging network service quality that integrates user satisfaction. First, considering the coupled dynamics of the EV charging network, we construct a service quality evaluation index system. Then, using a combination of the entropy weight method (EWM) and the analytic hierarchy process (AHP) with game theory, we determine the composite weights for the index system. Finally, we use the improved R-method evaluation model to theoretically verify the charging network in a particular area. The results show that the evaluation method proposed in this paper realizes differentiated evaluation for different planning schemes, highlights the weak links in the operation process of the charging network, and provides a theoretical reference for decision making about charging station planning. [ABSTRACT FROM AUTHOR]

Time-based theorizing has recognized the importance of temporal complexity but tends to reduce it to tensions and conflicts among but two static temporal demands. We recon-ceptualize temporal complexity as a dynamic interplay of multiple—that is, more than two—divergent temporal demands. Based on this reconceptualization, we develop a theory of organizational Eigenzeit, which illuminates how organizations “carve out” a time of their own amid temporal complexity. We propose a process model that explains how organizations navigate distinct constellations of temporal complexity by enacting varying degrees of temporal uncoupling and differentiation. Our model enables us to derive four generic modes of Eigenzeit—entrained, ambitemporal, agile, and pluritemporal—and theorize their characteristics and implications. We argue that grand challenges, such as climate change, require organizations to shift to more advanced modes of Eigenzeit, and detail why such a shift might be difficult to enact. Our paper contributes to time-based theorizing by specifying the distinct nature of temporal complexity and redirecting extant research toward examining different modes of Eigenzeit and their implied dynamics and consequences. [ABSTRACT FROM AUTHOR]

This paper examines the incentive to register for deceased organ donation under alternative organ allocation priority rules, which may prioritize registered donors and/or patients with higher valuations for organ transplantation. Specifically, the donor priority rule grants higher priority on the organ waiting list to those who have previously registered as donors. The dual-incentive priority rules allocate organs based on donor status, followed by individual valuations within the same donor status, or vice versa. Both theoretical and experimental results suggest that the efficacy of the donor priority rule and the dual-incentive priority rules critically depends on the information environment. When organ transplantation valuations are unobservable prior to making donation decisions, the hybrid dual-incentive rules generate higher donation rates. In contrast, if valuations are observable, the dual-incentive priority rules create unbalanced incentives between high- and low-value agents, potentially undermining the efficacy of the hybrid dual-incentive rules in increasing overall donation rates. This paper was accepted by Marie Claire Villeval, behavioral economics and decision analysis. Funding: This research is supported by the National Natural Science Foundation of China [Grants 72173103, 72373127, and 71988101], the Singapore Ministry of Education (MOE) Academic Research Fund Tier 1 [RG57/20], and the Open Foundation of Key Laboratory of Interdisciplinary Research of Computation and Economics (Shanghai University of Finance and Economics), Ministry of Education of China. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.01530. [ABSTRACT FROM AUTHOR]

This paper presents N-bipolar soft expert (N-BSE) sets, a novel framework designed to enhance multi-attribute group decision-making (MAGDM) by incorporating expert input, bipolarity, and non-binary evaluations. Existing MAGDM approaches often lack the ability to simultaneously integrate positive and negative assessments, especially in nuanced, multi-valued evaluation spaces. The proposed N-BSE model addresses this limitation by offering a comprehensive, mathematically rigorous structure for decision-making (DM). Fundamental operations of the N-BSE model are defined and analyzed, ensuring its theoretical consistency and applicability. To demonstrate its practical utility, the N-BSE model is applied to a general case study on sustainable energy solutions, illustrating its effectiveness in handling complex DM scenarios. An algorithm is proposed to streamline the DM process, enabling systematic and transparent identification of optimal alternatives. Additionally, a comparative analysis emphasizes the advantages of the N-BSE model over existing MAGDM frameworks, highlighting its capacity to integrate diverse expert opinions, evaluate both positive and negative attributes, and support multi-valued assessments. By bridging the gap between theoretical development and practical application, this paper contributes to advancing DM methodologies. [ABSTRACT FROM AUTHOR]

Uncertainty exists in reality decision-making problems. Therefore, fuzzy theories, fuzzy intervals, intuitionistic fuzzy, and Z-numbers have been proposed and successfully applied to multi-criteria decision-making (MCDM). However, since the information presented in the Z-numbers method is the subjective opinion of the decision-maker, the problem of overestimating or underestimating the reliability of the information may occur in both individual and group decision-making. The Extended Z-numbers (ZE-numbers) method was proposed in 2021 to solve this problem; hence, the decision-making process no longer relies on subjective opinions only but seeks external experts related to the problem to further vote on the evaluation value given by the internal decision-maker, in order to modify the information's reliability, and thus to obtain a more realistic result. This paper combines the ZE-numbers method with the improved Elimination et Choix Traduisant la Realite II (ELECTRE II) proposed in 2022 and proposes a new MCDM method based on ZE-numbers, named ZE-ELECTRE II. The green supplier selection problem was used as an illustrative example. Meanwhile, the close analysis in this paper examines two primary dimensions of variability: (1) simulation of external expert voting situations to analyze the variations in information reliability and decision-making results and to cross-compare them with other MCDM methods; (2) investigation of the impact of internal preferences, as reflected through systematic adjustments to the weights of the evaluation criteria. The results show that uncertainty of information, reliability, and the perspectives of different decision-makers and expert groups can be considered through ZE-numbers. The proposed ZE-ELECTRE II is applicable to group decision-making, validates the robustness of the process, and is suitable for dynamic decision-making under varying decision-maker preferences. Furthermore, using the ZE-numbers along with the MCDM method can obtain more flexibility and more reliable results. [ABSTRACT FROM AUTHOR]

In modern warfare, the accurate prediction of the intercept time and intercept point of the interceptor is the key to achieving penetration. Aiming at this problem, firstly, a convolutional neural network (CNN) is used to automatically extract high-level features from the data, and then these features are used as the input of support vector regression (SVR) for regression prediction. The Harris Hawk optimization (HHO) is used to optimize the hyperparameters of SVR, and the HHO-CNN-SVR algorithm is proposed. In order to verify the effectiveness of the algorithm for the prediction of the interception point and interception time, this paper constructs a dataset to test the method of simulating the missile interception maneuvering target. Compared with BP, ELM, SVR, HHO-SVR, and CNN-SVR models, the HHO-CNN-SVR model has outstanding performance in prediction accuracy and stability, especially for the interception time. The error is the smallest, and the error fluctuation is small. The MAE of the prediction result is only 0.0139 s; in the interception point prediction, the error of the range and elevation direction is significantly lower than that of the models used for comparison. The MAE in the range direction is 2.3 m, and the MAE in the elevation direction is 2.01 m, which meet the engineering requirements. The HHO-CNN-SVR model has strong prediction accuracy and stability in interception time and interception point prediction. In addition, different control strategies are used to construct a new prediction set, and noise is added to the prediction set. The HHO-CNN-SVR algorithm can maintain good prediction results. The results show that the HHO-CNN-SVR model proposed in this paper has strong generalization ability and high robustness, which can provide reliable support for penetration decision making and defense system optimization. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to develop a conceptual framework that jointly considers Environmental, Social and Governance (ESG) factors and organisational resilience (OR) components to ameliorate organisations' understanding of sustainability's overall requirements and related decision-making processes. Design/methodology/approach: This paper combines ESG and OR through a 3x3 conceptual matrix, where ESG factors are listed along the vertical axis and OR components along the horizontal axis. This results in nine quadrants, which have been read according to two arrangements: (1) static, looking at the specific characteristics of each single quadrant, and (2) dynamic, investigating the relationships between the different quadrants according to the system theory (ST) lens. Findings: The integration between ESG and OR results in nine organisational typologies, each characterised by a specific focus: (1) green visioning, (2) eco ethos, (3) climate guard, (4) inclusive strategy, (5) empathy ethos, (6) community shield, (7) ethical blueprint, (8) integrity ethos and (9) compliance guard. These typologies and related focuses determine the different strategic options of organisations, the decision-making emphasis concerning ESG factors and OR components and the organisation's behaviour concerning its internal and external environment. According to ST, the nine typologies interact with each other, emphasising the existence of interconnectedness, interdependence and cascading effects between ESG and OR. Originality/value: The paper represents a unique attempt to interrelate ESG factors and OR components according to a ST lens, emphasising the dynamic nature of their interactions and organisations' need for continuous adaptation and learning to make decisions that create sustainable long-term value. [ABSTRACT FROM AUTHOR]

The study of electricity resource adequacy (i.e. quantifying the risk of future resource shortfall) typically uses standard metrics such as Loss of Load Expectation and Expected Energy Unserved. A range of possible risk model outputs are critiqued in this paper as a basis for decision analysis on capacity procurement, starting with the current standard approach (in which risk is commonly monetised as EEU multiplied by VOLL), and moving on to alternatives such as risk averse metrics and wider visualisations of risk profile. There are concerns in principle from a decision analytic perspective with using expected money as a utility function. Also, while there is interest in use of risk-averse metrics such as Conditional Value-at-Risk (CVaR), these might not capture all aspects of risk profile that are of interest. To obtain a comprehensive picture of the risk profile of a system, it is necessary to visualise a range of distributions of outcome measures, rather than using single number metrics. Providing such visualisations is likely not to provide the basis for a transparent risk standard, and options are discussed such as setting a level for a single metric that is occasionally revised according to changing profile of resources. Any such metric-based approach should also be supplemented by assessment of wider aspects of risk profile, for instance through examining distributions of outcome metrics. The inherent uncertainty, due to data limitations and imperfect representation of the system in the model, should also be acknowledged appropriately in estimation of such metrics. [ABSTRACT FROM AUTHOR]

The pull-to-center (PTC) effect refers to newsvendors' behaviour of under-/overordering in high-/low-profit conditions. This paper examines the impact of peer effects on newsvendors' decision behaviour and performance through laboratory experiments. We consider a paired newsvendor setting where subjects are randomly divided into pairs and are informed of their peers' decisions and performance when they make order decisions, where peer effects arise. We find that subjects are unintentionally influenced by and intentionally learn from their peers' decisions and ultimately manifest a peer-chasing behaviour (i.e., to chase peers' decisions). Comparing with the single newsvendor setting, we further demonstrate the impact of the peer effect: (i) the peer-chasing behaviour can mitigate the PTC effect and hence improve the profit; (ii) the peer-chasing behaviour can counter the adverse effect of the demand-chasing heuristic; and (iii) the peer-chasing behaviour can theoretically reduce the variance of subjects' order quantities and lead to a profit improvement. This paper contributes to the literature by first identifying the peer-chasing behaviour(s) and introducing a new debiasing strategy to mitigate the PTC effect via peer effects. We also propose an approach to link the level and adjustment behaviours, revealing how various adjustment behaviours affect the corresponding level behaviour. [ABSTRACT FROM AUTHOR]

The A* search algorithm is widely utilized to evaluate the shortest path in a given network. However, in a traditional A* search algorithm, the nodes are assumed to have crisp values, i.e., a single value. This assumption may not hold in many real-world scenarios where uncertainty or ambiguity is involved. In such cases, an interval-valued Neutrosophic Pythagorean (IVNP) environment can provide a more sound and accurate representation. Interval-valued Neutrosophic Pythagorean sets (IVNPS) are an effective way to model vague and imprecise data, which is prevalent in executive problems. These sets provide a more flexible way to capture uncertainty by allowing the values of nodes in the graph to vary within certain intervals rather than having fixed values. This interval representation can effectively handle imprecise or incomplete information and is a powerful tool in executive processes. In this research paper, we proposed an improved A* search algorithm that takes advantage of the interval-valued neutrosophic Pythagorean environment. This algorithm aims to evaluate the shortest path in a graph under uncertainty and ambiguity. The proposed algorithm incorporates the IVNPS theory into the A* search framework to handle the uncertainty in node values and edge weights. It utilizes the concept of neutrosophic Pythagorean distance to calculate the heuristic function and make informed decisions on the next node to expand. [ABSTRACT FROM AUTHOR]

This paper aims to introduce a novel idea of possibility multi-fuzzy soft ordered semigroups for ideals and interior ideals. Various results, formulated as theorems based on these concepts, are presented and further validated with suitable examples. This paper also explores the broad applicability of possibility multi-fuzzy soft ordered semigroups in solving modern decision-making problems. Furthermore, this paper explores various classes of ordered semigroups, such as simple, regular, and intra-regular, using this innovative method. Based on these concepts, some important conclusions are drawn with supporting examples. Moreover, it defines the possibility of multi-fuzzy soft ideals for semiprime ordered semigroups. [ABSTRACT FROM AUTHOR]

This paper investigates the complex interplay between emotional intelligence (EI) and consumer decision-making processes. It defines and discusses the key elements of emotional intelligence, including emotional awareness, emotional facilitation, emotional knowledge, and reflection. The study explained how emotions, through all stages of the decision process--from problem recognition to information search, evaluation of alternatives, purchase decision, and finally, post-purchase evaluation--metabatically affect consumers' preferences, judgments, and behaviors. It tackles the interface between psychological variables, habitual behaviors, and external influences modulating consumer choices. The paper will review the theoretical frameworks of trait emotional intelligence theory and the ability model and discuss both qualitative and quantitative methodologies used in the study of EI and consumer behavior. These findings underline the impact of EI on consumer outcomes and shed light on its implications in marketing strategies that include personalized experiences and emotional data analysis. The paper finally provides some practical recommendations on how to integrate EI into business strategies and infers further research directions regarding the evolving role that emotional intelligence could assume in consumer behavior, including the impact of digital emotional intelligence and authenticity. [ABSTRACT FROM AUTHOR]

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Environmental perception is a key technology for autonomous driving, enabling vehicles to analyze and interpret their surroundings in real time to ensure safe navigation and decision-making. Multi-sensor information fusion, which integrates data from different sensors, has become an important approach to overcome the limitations of individual sensors. Each sensor has unique advantages. However, its own limitations, such as sensitivity to lighting, weather, and range, require fusion methods to provide a more comprehensive and accurate understanding of the environment. This paper describes multi-sensor information fusion techniques for autonomous driving environmental perception. Various fusion levels, including data-level, feature-level, and decision-level fusion, are explored, highlighting how these methods can improve the accuracy and reliability of perception tasks such as object detection, tracking, localization, and scene segmentation. In addition, this paper explores the critical role of sensor calibration, focusing on methods to align data in a unified reference frame to improve fusion results. Finally, this paper discusses recent advances, especially the application of machine learning in sensor fusion, and highlights the challenges and future research directions required to further enhance the environmental perception of autonomous systems. This study provides a comprehensive review of multi-sensor fusion technology and deeply analyzes the advantages and challenges of different fusion methods, providing a valuable reference and guidance for the field of autonomous driving. [ABSTRACT FROM AUTHOR]

Purpose: In recent decades, infrastructure has continued to develop as an important basis for social development and people's lives. Resource management of these large-scale projects has been immensely concerned because dozens of construction enterprises (CEs) often work together. In this situation, resource collaboration among enterprises has become a key measure to ensure project implementation. Thus, this study aims to propose a systematic multi-agent resource collaborative decision-making optimization model for large projects from a matching perspective. Design/methodology/approach: The main contribution of this work was an advancement of the current research by: (1) generalizing the resource matching decision-making problem and quantifying the relationship between CEs. (2) Based on the matching domain, the resource input costs and benefits of each enterprise in the associated group were comprehensively analyzed to build the mathematical model, which also incorporated prospect theory to map more realistic decisions. (3) According to the influencing factors of resource decision-making, such as cost, benefit and attitude of decision-makers, determined the optimal resource input in different situations. Findings: Numerical experiments were used to verify the effectiveness of the multi-agent resource matching decision (MARMD) method in this study. The results indicated that this model could provide guidance for optimal decision-making for each participating enterprise in the resource association group under different situations. And the results showed the psychological preference of decision-makers has an important influence on decision performance. Research limitations/implications: While the MARMD method has been proposed in this research, MARMD still has many limitations. A more detailed matching relationship between different resource types in CEs is still not fully analyzed, and relevant studies about more accurate parameters of decision-makers' psychological preferences should be conducted in this area in the future. Practical implications: Compared with traditional projects, large-scale engineering construction has the characteristics of huge resource consumption and more participants. While decision-makers can determine the matching relationship between related enterprises, this is ambiguous and the wider range will vary with more participants or complex environment. The MARMD method provided in this paper is an effective methodological tool with clearer decision-making positioning and stronger actual operability, which could provide references for large-scale project resource management. Social implications: Large-scale engineering is complex infrastructure projects that ensure national security, increase economic development, improve people's lives and promote social progress. During the implementation of large-scale projects, CEs realize value-added through resource exchange and integration. Studying the optimal collaborative decision of multi-agent resources from a matching perspective can realize the improvement of resource transformation efficiency and promote the development of large-scale engineering projects. Originality/value: The current research on engineering resources decision-making lacks a matching relationship, which leads to unclear decision objectives, ambiguous decision processes and poor operability decision methods. To solve these issues, a novel approach was proposed to reveal the decision mechanism of multi-agent resource optimization in large-scale projects. This paper could bring inspiration to the research of large-scale project resource management. [ABSTRACT FROM AUTHOR]

The paper aims to examine the relationships between behavioural biases (such as overconfidence and herding) and the rational behaviour of Australian female consumers when making financial decisions. In doing so, the paper showcases the financial illiteracy of Australian female consumers when confronted with irregularities within the Australian financial markets. From a theoretical standpoint, the study adopts the notions of the adaptive market hypothesis (AMH) to understand the reasoning behind the relationships between behavioural biases (such as overconfidence and herding) and the rational behaviour of Australian female consumers when making decisions rationally. Using a quantitative approach, a structural equation modelling (SEM) was conducted on the proposed theoretical framework with a cleaned dataset of 357 Australian female consumers, which revealed that behavioural biases significantly influence each stage of rational decision-making when making financial decisions. More precisely, the structural equation modelling (SEM) showcases that herding behaviour has a significant positive relationship with the information search and evaluation of alternative stages when making financial decisions. However, overconfidence behaviour has a significant negative relationship with demand identification and evaluation of alternative stages when making financial decisions. Moreover, the findings also showcase that the proposed theoretical model closely fits with the data utilised, indicating that Australian female consumers do follow rational decision-making when making financial decisions. Additionally, the findings revealed that the education and income levels of Australian female consumers positively influence the stages of rational decision-making. The findings also contend that Australian female consumers have a risk-averse attitude (i.e., within three key hypothetical scenarios) towards financial decisions due to the presence of financial illiteracy. Hence, it is strongly suggested that financial institutions highlight the calculative benefits and returns from financial product purchases in advertising and promotions in a way that appeals to female consumer segments. [ABSTRACT FROM AUTHOR]

In the evolving field of decision-making, the continuous advancement of technologies and methodologies drives the pursuit of more reliable tools. Decision support systems (DSS) provide information to make informed choices and multi-criteria decision analysis (MCDA) methods are an important component of defining decision models. Despite their usefulness, there are still challenges in making robust decisions in dynamic environments due to the varying performance of different MCDA methods. It creates space for the development of techniques to aggregate conflicting results. This paper introduces a fuzzy ranking approach for aggregating results from multi-criteria assessments, specifically addressing the limitations of current result aggregation techniques. Unlike conventional methods, the proposed approach represents rankings as fuzzy sets, providing detailed insights into the robustness of decision problems. The study uses green supplier selection as a case study, examining the performance of the introduced approach and the robustness of its recommendations within the sustainability field. This study offers a new methodology for aggregating results from multiple evaluation scenarios, thereby enhancing decision-maker awareness and robustness. Through comparative analysis with traditional compromise solution methods, this paper highlights the limitations of current approaches and indicates the advantages of adopting fuzzy ranking aggregation. This study significantly advances the field of decision-making by enhancing the understanding of the stability of decision outcomes. [ABSTRACT FROM AUTHOR]

Spatial optimization is an integral part of GIS and spatial analysis. It involves making various decisions in space, ranging from the location of public facilities to vehicle routing and political districting. While useful, such problems (especially large problem instances) are often difficult to solve using general mathematical programming (due to their generality). Traditionally, an alternative solution method is Lagrangian relaxation, which, if well-designed, can be fast and optimal. One has to derive the Lagrangian dual problem and its (sub)gradients, and move towards the optimal solution via a search process such as gradient descent. Despite its merits, Lagrangian relaxation as a solution algorithm requires one to derive the (sub)gradients manually, which is error-prone and makes the solution algorithm difficult to develop and highly dependent on the model at hand. This paper aims to ease the development of Lagrangian relaxation algorithms for GIS practitioners by employing the automatic (sub)gradient (autograd) computation capabilities originally developed in modern Deep Learning. Using the classic p-median problem as an example, we demonstrate how Lagrangian relaxation can be developed with paper and pencil, and how the (sub)gradient computation derivation can be automated using autograd. As such, the human expert only needs to implement the Lagrangian problem in a scientific computing language (such as Python), and the system can find the (sub)gradients of this code, even if it contains complex loops and conditional statements. We verify that the autograd version of the algorithm is equivalent to the original version with manually derived gradients. By automating the (sub)gradient computation, we significantly lower the cost of developing a Lagrangian algorithm for the p-median. And such automation can be applied to numerous other optimization problems. [ABSTRACT FROM AUTHOR]

Purpose: Stakeholder capitalism has been proposed as an alternative way of thinking about business purpose and value creation. However, stakeholder capitalism can only work as an alternative model of business if all stakeholders and their interests are visible to and taken seriously by managers. The purpose of this paper is to untangle the challenges that invisible, marginalized and powerless stakeholders pose for theorizing about stakeholder capitalism. Design/methodology/approach: This paper is conceptual. The authors first briefly outline the promise of stakeholder capitalism for addressing pressing questions about value creation and stakeholder welfare. The authors then conceptualize stakeholder invisibility as the outcome of a particular stakeholder being both powerless and marginal through the prism of moral intensity theory and one of its elements: proximity. This study discusses the ways in which managers can make invisible stakeholders more visible in their decision-making. Findings: For managers truly to manage for stakeholders, as anticipated by stakeholder capitalism, all stakeholders and stakeholder interests must be visible to them. This study analyzes why sometimes they are not, how they can be made more visible and why stakeholder visibility matters for stakeholder capitalism. This study proffers three principles for business practice: ethical commitments to reduce stakeholder invisibility, analyses of business strategies to surface the contributions of marginalized and invisible stakeholders and taking rights seriously. Originality/value: This study provides a new perspective on stakeholder capitalism by linking the challenge in operationalizing it to the problems of stakeholder invisibility and marginality. [ABSTRACT FROM AUTHOR]

This study investigates the neural and physiological mechanisms underlying External Referent Decision Awareness (ERDA) within organizational contexts, focusing on hierarchical roles (Head, Peer, Staff). Twenty-two professionals participated, and electroencephalographic (EEG frequency band: Delta, Theta, Alpha, Beta, Gamma) and autonomic indices (skin conductance and cardiovascular indices) were recorded, while personality traits and decision-making styles were assessed. Results revealed higher Delta and Theta activation in the left temporo-parietal junction (TPJ) during Peer-related decisions, reflecting increased social cognition and ambiguity regulation in those contexts. Gamma activity, associated with high-order cognitive processes, was prominent in the left frontal cortex across all roles, indicating complex decision evaluation. These findings underscore the complexity of low-frequency bands (Delta and Theta), involved in emotional regulation and social cognition, while high-frequency bands (Gamma) reflect cognitive integration and decision complexity. Furthermore, autonomic data showed higher Skin Conductance Levels (SCL) for Head decisions, suggesting greater emotional involvement.The findings revealed a significant negative correlation between avoidant decision-making styles and the neural and behavioral evaluations of leader decisions, suggesting reduced engagement of neurocognitive systems involved in reward processing and evaluative judgment in individuals with a tendency to avoid decision-making. Additionally, higher extraversion correlated with more favorable evaluations of decisions made by Staff, potentially indicating greater activation in neural circuits associated with social reward and group dynamics. In conclusion, these findings suggest that neural activity and personality traits interact to shape hierarchical decision-making awareness, highlighting the need for tailored leadership and decision-making strategies in organizations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 International Brain Research Organization (IBRO). Published by Elsevier Inc. All rights reserved.)

Human-computer collaboration serves as a high-quality method to achieve optimal decisions in the workplace. However, there are relatively few existing papers that focus on how to effectively aggregate the viewpoints of different individuals. Contemporary research suggests that decision confidence bears a positive correlation with decision accuracy, thereby indicating that it can serve as a weighting parameter for aggregating viewpoints. This paper endeavors to quantitatively estimate decision confidence through the application of electroencephalogram (EEG). In this paper, we designed an animal recognition task to measure different decision confidence levels. The success of the task design was demonstrated by the comparison result of behavioral data and EEG at different confidence levels. In addition, then a neural network called channel attention based thinker-invariant DenseNet was proposed to predict confidence levels, with an average accuracy of 77.84%, higher than the results of existing models. Moreover, the regions of the brain associated with decision confidence, found by visualizing the channel attention module of our model, are consistent with existing studies., (© 2025 Author(s). Published under an exclusive license by AIP Publishing.)

The growing burden of cancer, with its costly and difficult to endure therapies, underscores the urgent need for individualized care that enhances patients' quality of life. Effective individualized treatment planning requires collaboration between healthcare professionals and patients (and their relatives), focusing on patients' personal values and preferences through shared decision-making. In practice, oncology care predominantly follows clinical guidelines, which, while reducing practice variation, may not account for individual patient differences in health status and preferences. Moreover, guidelines often emphasize medical endpoints such as disease-free survival, overlooking quality of life and treatment burden considerations crucial to many patients. This can result in unfitting treatment choices and decisional regret. The Integrated Oncological Decision-making Model (IODM) addresses these challenges by incorporating three essential domains for treatment decision-making: medical treatment options, patient goals and preferences, and general health status. The IODM aims to integrate these domains into the decision-making process, ensuring that treatment plans are tailored to individual patient needs. Initial implementation of the IODM is shown promising results, with a significant proportion of patients receiving less invasive, more appropriate treatments without compromising survival outcomes. The IODM has demonstrated its potential to facilitate patient-centred care, reduce unnecessary treatments, and improve patient outcomes. Continued implementation and evaluation of the IODM across various oncological settings are essential to refine the model and ensure its integration into routine clinical practice, ultimately enhancing the quality and effectiveness of cancer care., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective: College students are considered as a high-risk age group for contracting HPV. HPV vaccines are proved to play a major role in preventing Cervical cancer and related cancers among both men and women. This study aimed to explore influencing factors in intention making-decision of Human papillomavirus vaccine in Chinese college students., Methods: A descriptive qualitative research based on naturalistic inquiry in Daqing City, Heilong Jiang Province from October to November 2022 was conducted. Semi-structured interviews with Chinese college students. We used thematic framework analysis with NVivo 12.0 software to transcribe and analyze the interview recordings., Results: We conducted 20 in-depth interviews. Four main themes with 14 subthemes were identified: (a) potential risks of HPV infection; (b) susceptibility factors of HPV infection; (c) difficulties in HPV vaccination; (d) decision support for HPV vaccination., Conclusion: Most college students expressed hesitancy toward receiving the HPV vaccine. The government should strengthen the advocacy of the benefits of the vaccine to increase the HPV vaccination rate., Practice Implications: Healthcare providers and the government should take some strategies to promote facilitate factors to increase HPV vaccination rates., Competing Interests: Declaration of Competing Interest We have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Jaywalking, as a hazardous crossing behavior, leaves little time for drivers to anticipate and respond promptly, resulting in high crossing risks. The prevalence of Autonomous Vehicle (AV) technologies has offered new solutions for mitigating jaywalking risks. In this study, we propose a risk-aware deep reinforcement learning (DRL) approach for AVs to make decisions safely and efficiently in jaywalker-vehicle interactions. Notably, a risk prediction module is incorporated into the traditional DRL framework, making the AV agent risk-aware. Considering the complexity of jaywalker-vehicle conflicts, an encoder-decoder model is adopted as the risk prediction module, which comprehensively integrates multi-source data and predicts probabilities of the final conflict severity levels. The risk-aware DRL approach is applied in a simulated environment established in Anylogic, where the motion features of jaywalkers and vehicles are calibrated using real-world survey data. The trained driving policies are evaluated from perspectives of safety and efficiency across three scenarios with escalading levels of jaywalker volume. Regarding safety performance, the Baseline policy performs the worst in "medium jaywalker volume" scenario and "high jaywalker volume" scenario, while our Proposed risk-aware method outperforms the other methods, with the "low TTC ratio" metric stabilizing near 0.08. Moreover, as the scenario gets more complex, the superiority of our Proposed risk-aware policy gets more evident. In terms of efficiency performance, our Proposed risk-aware policy ranks the second best, achieving an "AV delay" metric around 8.1 s in the "medium jaywalker volume" scenario and 8.5 s in the "high jaywalker volume" scenario. In practice, the proposed risk-aware DRL approach can help AV agents perceive potential risks in advance and navigate through potential jaywalking areas safely and efficiently, further enhancing pedestrian safety., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Decision-making (DM) is a complex cognitive behavior that involves gathering information and assessing options to identify choices under risky and uncertain conditions. Mild Cognitive Impairment (MCI) is a construct that includes a constellation of symptoms ranging from behavioral to cognitive impairments. This cluster of symptoms is frequently associated with poor decision-making. This study aimed to examine decision-making in pathological aging, specifically MCI. Therefore, we conducted a systematic review and meta-analysis to evaluate these relationships. According to the PRISMA 2020 Statement, nine studies were selected for the systematic review and eight for the meta-analysis. The results highlighted that MCI is associated with impaired decision-making in risky and ambiguous situations. The systematic review reported that MCI was associated with impaired decision-making in ambiguous and in risky conditions. In contrast, the meta-analysis showed significant differences in overall decision-making and particularly in ambiguous conditions. This difficulty may be due to different impairments that affect MCI. The difficulty in advantageous decision-making could be due to different brain alterations in MCI, which could lead to problems in tasks requiring feedback-based responses. These findings advance our understanding of decision-making in aging and suggest how decision-making alterations in MCI would affect the totality of executive functions and daily activities., Competing Interests: Declaration of competing interest This manuscript has not been published and is not under consideration for publication elsewhere. The paper has been seen and approved by all authors. All the authors contributed to the development and draft of the manuscript. We have no conflict of interest to disclose., (Copyright © 2024. Published by Elsevier B.V.)

Background: In May 2020, England implemented "deemed consent" legislation, to make it easier for individuals to donate their organs and convey their decision when alive. Families are supposed to support the decision but can still override it if they disagree. We aimed to learn more about this changed role when families were approached about organ donation., Methods: A qualitative study using semi-structured interviews with families, feedback from nurses, comparisons with audit data, and public involvement. We used framework analysis with a health systems perspective and utilitarian theory to explore if the law worked., Findings: 103 participants were interviewed representing 83 potential donation cases. In 31/83 (37%) cases donation was fully supported, in 41/83 (49%) cases families supported retrieval of some organs, tissues and procedures, and in 11/83 (13%) cases families declined completely. Themes explaining why the law was not (yet) working included: Understanding and agreeing the family's role, confusion about deemed consent, not supporting the deceased expressed decisions, organ donation as too much of a harm, the different experiences of donation pathways, transition from end-of-life to organ donation discussions, experiences of 'consent', paperwork and processes. Families frequently questioned if their relative wanted to have a surgery rather than supporting the person who died to save lives., Conclusion: Families use the unique experience of their relative dying in intensive care to create alternate narratives whereby the outcome satisfies their own utility and not necessarily those of the potential donor. New public ongoing media campaigns crafted to be more supportive of organ donation as a benefit to transplant recipients could help families overcome the many difficulties they encounter at the bedside., Implications for Clinical Practice: The soft opt-out policy has not empowered nurses to help families at their most vulnerable to increase their support for and consent to deceased organ donation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

In India, adolescent girls are highly vulnerable, facing risks that hinder their healthy development into young women. India's Rajiv Gandhi Scheme for Empowerment of Adolescent Girls (SABLA) aims to address these challenges by promoting health awareness and encouraging decision-making autonomy, fostering women's development. Using data from the nationally representative India Demographic and Health Survey (IDHS) and exploiting exogenous variations in the program's rollout across districts and birth cohorts, we provide evidence that exposure to SABLA increases the likelihood of young female participants using family planning and modern family planning methods. Furthermore, it indicates that participants are more likely to adopt a healthier diet, characterized by increased consumption of green leafy vegetables and reduced intake of fried foods. Additionally, the program empowers participants to have control over their financial resources. The findings also reveal a significant reduction in the likelihood of anemia among women who were exposed to the program. Notably, our results suggest that SABLA contributes to delaying women's marriage and their first childbirth. We find that women exposed to the program have fewer children than their counterparts and face fewer pregnancy losses. To account for COVID-19 disruptions, we include a COVID dummy variable. While the pandemic affected family planning, nutrition, and anemia, SABLA's positive impact on health and empowerment remains strong. Notably, it did not affect women's role in household decision-making, highlighting its lasting effect on empowerment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)