Decision-making Process Research Articles

Shared decision-making competency and its associated factors among palliative care nurses: a cross-sectional study in China

BackgroundThe role of nurses in shared decision-making (SDM) within the healthcare decision-making process has increasingly gained attention The ability of nurses to engage in effective SDM can significantly impact patient outcomes and satisfaction with care. Given the palliative care continues to evolve, this study aimed to investigate the level of SDM competency and explore associated factors among palliative care nurses in China.MethodsA cross-sectional study was conducted between June and July 2024. The convenience sample was recruited from five online Chinese palliative care nurse groups. Anonymous data were collected through a self-designed sociodemographic questionnaire, the Shared Decision-Making Competency Scale (SDMCS) and the Empathy Ability Scale (EAS). Multivariate linear regression was performed to explore the factors associated with SDM competency.ResultsA total of 429 palliative care nurses from 30 provinces or municipalities across China participated in this study. The Chinese palliative care nurses had a high level of SDM competency (mean total SDMCS 211.72 ± 25.75) and high empathic ability (EA), (mean total EAS 132.63 ± 20.30). There was a positive correlation between EA and SDM competency (r = 0.704, P < 0.01). The EA (β = 0.683, P<0.001), experiences of SDM training (β=-0.155, P<0.001) and educational background (β=-0.142, P = 0.007) were statistically significant factors influencing nurses’ SDM competency based on multiple linear regression analysis. These associated factors could explain 55.4% of the difference in SDM competency.ConclusionsThis study found that palliative care nurses in China exhibited high levels of SDM competency and a high level of EA. Nurses with postgraduate education, SDM training experience, and high EA demonstrated higher levels of SDM competency compared to those without these attributes. The findings underscore the importance of incorporating structured SDM training programs in nursing education and professional development initiatives, particularly focused on building empathy skills.

AI-Driven Governance: Transforming Public and Addressing Legacy Issues in Post-Colonial Africa

This paper examines the transformational potentials that artificial intelligence (AI) may hold to reshape our public policies and methods of administration in the unique post-colonial context of Africa. We thus seek to unearth how AI technologies can be employed at a continental scale in the remedy of legacy issues arising from colonialism including; governance inefficiency, literacy gaps, and inequitable service delivery across the continent. From critically analyzing the application of AI in various public sectors, our research seeks to unveil opportunities for AI in inclusive decision-making processes to improve transparency as well as tailoring public service delivery to the diversified needs of African populations. The paper describes the way forward in the adoption of AI solutions that involve issues on a variety of considerations, infrastructure requirements, financial obstacles, and capacity development, among others. Highlighting the potential of AI in governance, this research underscores the place of local innovation stakeholder engagement, and international collaboration in assuring that AI plays out as a development lever for both sustainable development and empowerment in post-colonial Africa.

Examining generative AI user continuance intention based on the SOR model

PurposeThe purpose of this research is to examine generative artificial intelligence (AI) user continuance intention based on the stimulus-organism-response model.Design/methodology/approachWe adopted a mixed method of structural equation modeling and fuzzy-set qualitative comparative analysis to conduct data analysis.FindingsThe results found that generative AI content quality (perceived personalization, perceived accuracy and perceived credibility) and system quality (perceived interactivity, perceived anthropomorphism and perceived intelligence) affect sense of empowerment and satisfaction, both of which further determine continuance intention.Originality/valueExtant research has identified the effect of flow, trust and parasocial interaction on generative AI user continuance, but it has seldom disclosed the internal decisional process of generative AI user continuance intention. This research tries to fill this gap, and the results enrich the extant research on generative AI user continuance.

Factors Influencing Coconut Growers’ Decision-Making Process in Fertilizer Application through the Lens of Theory of Planned Behavior and Self-Determination Theory

The small-scale, poor, rural coconut growers in Sri Lanka are generally characterized by low productivity. The low yield of coconuts is primarily attributed to the improper application of fertilizers. The decision to apply fertilizer is mainly depends on the growers’ change in behaviour, in turn, influenced by the growers’ salient beliefs. Assessing the beliefs plays a significant role in understanding why farmers behave differently in making decisions regarding fertilizer application. The Theory of Planned Behaviour (TPB) and Theory of Self Determination (TSD) were employed to find a more realistic solution to the above research problem. Hence, the current research was carried out to observe the relationship between the beliefs of the coconut growers and their behaviour with respect to fertilizer application in coconut fields. Results of the study reveal that perceived behavioural control was the highest contributor to developing the intention followed by the attitude. Further, there is no influence from the social pressure for the intention development. Moreover, intrinsic motivation predicts the fertilizer application behaviour from intention in stronger strength than that of extrinsic motivation. Therefore, it can be concluded that motivation influences the intention-behavioural relationship. Consequently, it gives evidence for policymakers to introduce policy guidelines in order to enhance the use of fertilizer efficiently and effectively. And, motivation does have a moderating effect on coconut growers’ fertilizer application behaviour.

Artificial intelligence-based cardiac transthyretin amyloidosis detection and scoring in scintigraphy imaging: multi-tracer, multi-scanner, and multi-center development and evaluation study.

Providing tools for comprehensively evaluating scintigraphy images could enhance transthyretin amyloid cardiomyopathy (ATTR-CM) diagnosis. This study aims to automatically detect and score ATTR-CM in total body scintigraphy images using deep learning on multi-tracer, multi-scanner, and multi-center datasets. In the current study, we employed six datasets (from 12 cameras) for various tasks and purposes. Dataset #1 (93 patients, 99mTc-MDP) was used to develop the 2D-planar segmentation and localization models. Dataset #2 (216 patients, 99mTc-DPD) was used for the detection (grade 0 vs. grades 1, 2, and 3) and scoring (0 and 1 vs. grades 2 and 3) of ATTR-CM. Datasets #3 (41 patients, 99mTc-HDP), #4 (53 patients, 99mTc-PYP), and #5 (129 patients, 99mTc-DPD) were used as external centers. ATTR-CM detection and scouring were performed by two physicians in each center. Moreover, Dataset #6 consisting of 3215 patients without labels, was employed for retrospective model performance evaluation. Different regions of interest were cropped and fed into the classification model for the detection and scoring of ATTR-CM. Ensembling was performed on the outputs of different models to improve their performance. Model performance was measured by classification accuracy, sensitivity, specificity, and AUC. Grad-CAM and saliency maps were generated to explain the models' decision-making process. In the internal test set, all models for detection and scoring achieved an AUC of more than 0.95 and an F1 score of more than 0.90. For detection in the external dataset, AUCs of 0.93, 0.95, and 1 were achieved for datasets 3, 4, and 5, respectively. For the scoring task, AUCs of 0.95, 0.83, and 0.96 were achieved for these datasets, respectively. In dataset #6, we found ten cases flagged as ATTR-CM by the network. Out of these, four cases were confirmed by a nuclear medicine specialist as possibly having ATTR-CM. GradCam and saliency maps showed that the deep-learning models focused on clinically relevant cardiac areas. In the current study, we developed and evaluated a fully automated pipeline to detect and score ATTR-CM using large multi-tracer, multi-scanner, and multi-center datasets, achieving high performance on total body images. This fully automated pipeline could lead to more timely and accurate diagnoses, ultimately improving patient outcomes.

Clinical validation and optimization of machine learning models for early prediction of sepsis

IntroductionSepsis is a global health threat that has a high incidence and mortality rate. Early prediction of sepsis onset can drive effective interventions and improve patients’ outcome.MethodsData were collected retrospectively from a cohort of 2,329 adult patients with positive bacteria cultures from a tertiary hospital in China between October 1, 2019 and September 30, 2020. Thirty six clinical features were selected as inputs for the models. We trained models in predicting sepsis by machine learning (ML) methods, including logistic regression, decision tree, random forest (RF), multi-layer perceptron, and light gradient boosting. We evaluated the performance of the five ML models and the evaluation metrics were: area under the ROC curve (AUC), accuracy, F1-score, sensitivity and specificity. The data of another cohort of 2,286 patients between October 1, 2020 and April 1, 2022 were used to validate the performance of the model performing best in the in the internal validation set. Shapley additive explanations (SHAP) method was applied to evaluate feature importance and explain the predictions of this model.ResultsOf the five machine learning models developed, the RF model demonstrated the best performance in terms of AUC (0.818), F1 value (0.38), and sensitivity (0.746). The RF model also has a comparable AUC (0.771) in the external validation set. The SHAP method identified procalcitonin, albumin, prothrombin time, and sex as the important variables contributing to the prediction of sepsis.DiscussionThe RF model we developed showed the greatest potential for early prediction of sepsis in admitted patients, which could aid clinicians in their decision-making process. Our findings also suggested that male patients with bacterial infections and high procalcitonin levels, lower albumin levels, or prolonged prothrombin times were more likely to develop sepsis.

Pathways and processes to adopting or switching to a single-dose HPV vaccination schedule in low- and middle-income countries: a qualitative study.

The World Health Organization (WHO) recently endorsed a single-dose human papillomavirus vaccination (HPVV) schedule, but adoption of the revised immunization schedule has not been uniform, notably in low- and middle-income countries (LMICs) with outsized cervical cancer burdens. We sought to characterize the sequence of events, as well as actors/institutions involved, that facilitate or impede country-level decision-making related to the HPVV schedule. From September 2023 to February 2024, we conducted 66 semi-structured interviews with national immunization stakeholders from 19 LMICs in Africa and Asia, sampled purposively by the maturity of the HPVV program and HPVV schedule. Using purposive text analysis to synthesize emerging insights from interviews, we qualitatively discerned a sequence of decision-making pathways and processes, including actors involved at each stage of the decision-making continuum, related to adoption or switch to an HPVV single-dose schedule. We identified six key sequential steps LMICs follow when deciding on an HPV dosing schedule. Firstly, the new recommendation emerges where WHO and the Strategic Advisory Group of Experts (SAGE) release new HPV vaccine recommendations. Second, the information is shared with the Ministry of Health (MoH) and Expanded Programme for Immunization (EPI). The MoH is informed, and they convene a National Immunization Technical Advisory Group (NITAG) to assess the new HPV vaccine schedule recommendation. Third, there is the review and consideration of evidence. The NITAG reviews evidence on factors like cost-effectiveness, supply chain, disease burden, and anticipated vaccine hesitancy. Fourth, the NITAG deliberates, achieves consensus, and submits a recommendation to the MoH, which endorses the NITAG's recommendation or requests additional evidence/information before rendering a final decision on the immunization schedule. Fifth and finally, the Interagency Coordination Committee (ICC) reviews the MoH's recommendation and provides final authorization before changes to the HPV immunization schedule is communicated to Gavi.

AI enhanced Smart Crop Selection

This paper presents an innovative approach to smart crop selection by utilizing machine learning algorithms, specifically XGBoost, combined with AI-driven insights. The proposed system integrates soil characteristics, climatic conditions, and market trends to offer precise recommendations tailored to optimize agricultural productivity and sustainability. By leveraging predictive analytics and neural networks, this system provides real-time yield predictions, pest risk analysis, and economic feasibility reports. The integration of advanced technologies bridges the gap between traditional farming methods and modern agricultural advancements, ensuring a comprehensive and efficient decision-making process for farmers. Key Words: Smart agriculture, machine learning, XGBoost, AI, crop recommendation, sustainable farming, predictive analytics

Prioritizing Transboundary Aquifers in the Arizona–Sonora Region: A Multicriteria Approach for Groundwater Assessment

Groundwater is vital to the well-being of over 20 million people in the nearly 2000-mile-long, arid U.S.–Mexico border region, supporting agricultural, industrial, domestic, and environmental needs. However, persistent droughts over the past two decades, coupled with increasing water demand and population growth, have significantly strained water resources, threatening the region’s water security. These challenges highlight the importance of comprehensive transboundary aquifer assessments, such as those conducted through the Transboundary Aquifer Assessment Program (TAAP), a collaborative effort between the U.S. and Mexico to evaluate shared aquifers. The TAAP focuses on four aquifers: the Santa Cruz and the San Pedro in Arizona and Sonora and the Mesilla and the Hueco Bolson in Texas, New Mexico, and Chihuahua. With the need for additional aquifer studies in this arid region, it is important to determine and prioritize which aquifers would benefit most from transboundary assessment. This study aims to prioritize aquifers in the Arizona–Sonora region based on multiple criteria. The results from this study reveal regional disparities in the need for transboundary aquifer studies, with some aquifers highlighted due to their groundwater use for economic activities, while others stand out for their population density and the transboundary nature of the hydrogeologic units. By leveraging publicly available data, this research established a priority ranking for these aquifers to support decision-making processes in identifying and addressing the most critical aquifers for binational assessment, while providing a framework that can be replicated across other shared aquifers between the U.S. and Mexico and elsewhere.

The impact of BIM on project time and cost: insights from case studies

Building Information Modelling (BIM) has emerged as a transformative tool in the Architecture, Engineering, and Construction (AEC) industries, offering significant potential to improve project efficiency and outcomes. This study explores the impact of BIM implementation on project time and cost by analyzing critical factors such as design errors, unbudgeted changes, Requests for Information (RFIs), labour dynamics, and scheduling across multiple case studies. Quantitative results reveal that BIM adoption reduces project timelines by an average of 20% and costs by 15%, while also decreasing design errors by 30% and RFIs by 25%. Furthermore, BIM enhances collaboration among stakeholders, improves design visualization, and facilitates better risk assessment, leading to more informed decision-making throughout the project lifecycle. These findings are synthesized into a decision-making framework that estimates the probability of errors, evaluates their potential time and cost implications, and ensures alignment with project budgets. The framework serves as a strategic guide for project teams to assess the suitability of BIM for specific projects, thereby optimizing decision-making processes and improving overall project performance. Additionally, the study examines the role of BIM in sustainability by reducing material waste and improving resource allocation. This study addresses a critical gap in the field by systematically evaluating the interrelationships among BIM's impacts on key project parameters, which have often been treated in isolation in prior research. The importance of this work lies in its provision of a structured methodology to harness BIM's capabilities, demonstrating its value in delivering significant time and cost efficiencies while enhancing project quality. By integrating empirical analysis with practical applications, this research contributes to the growing body of knowledge on BIM adoption and provides actionable insights for AEC professionals seeking to optimize project outcomes.

A cooperative jamming decision-making method based on multi-agent reinforcement learning

Electromagnetic jamming is a critical countermeasure in defense interception scenarios. This paper addresses the complex electromagnetic game involving multiple active jammers and radar systems by proposing a multi-agent reinforcement learning-based cooperative jamming decision-making method (MA-CJD). The proposed approach achieves high-quality and efficient target allocation, jamming mode selection, and power control. Mathematical models for radar systems and active jamming are developed to represent a multi-jammer and multi-radar electromagnetic confrontation scenario. The cooperative jamming decision-making process is then modeled as a Markov game, where the QMix multi-agent reinforcement learning algorithm is innovatively applied to handle inter-jammer cooperation. To tackle the challenges of a parameterized action space, the MP-DQN network structure is adopted, forming the basis of the MA-CJD algorithm. Simulation experiments validate the effectiveness of the proposed MA-CJD algorithm. Results show that MA-CJD significantly reduces the time defense units are detected while minimizing jamming resource consumption. Compared with existing algorithms, MA-CJD achieves better solutions, demonstrating its superiority in cooperative jamming scenarios.

Supported biopsychosocial self-management for back-related leg pain: a randomized feasibility study integrating a whole person perspective

BackgroundThere is limited high-quality research examining conservative treatments for back-related leg pain (BRLP). This feasibility study was done in preparation for a full-scale trial comparing a whole-person supported self-management intervention to medical care for chronic BRLP.MethodsParticipants were randomized to 12 weeks of individualized supported self-management delivered by physical therapists and chiropractors or medical care consisting of guideline-based pharmacologic care. Supported self-management was based on a behavioral model that used a whole person approach to enhance participants capabilities, opportunities, and motivations to engage in self-care. It combined BRLP education with psychosocial strategies (e.g., relaxed breathing, progressive muscle relaxation, guided imagery, communication skills) and physical modalities such as exercise and spinal manipulation therapy. Providers were trained to address participants’ individualized needs and use behavior change and motivational communication techniques to develop a therapeutic alliance to facilitate self-management. Feasibility was assessed using pre-specified targets for recruitment and enrollment, intervention delivery, and data collection over the six-month study period. In addition, areas for potential refinement and optimization of processes and protocols for the full-scale trial were assessed.ResultsWe met or exceeded nearly all feasibility targets. Forty-two participants were enrolled over a six-month period in 2022 and very few individuals declined participation due to preferences for one treatment. All but one participant received treatment and 95% of participants attended the minimum number of visits (self-management = 6, medical care = 2). At 12 weeks, 95% of participants in the self-management group reported engaging in self-management practices learned in the program and 77% of medical care participants reported taking medications as prescribed. Satisfaction with the self-management intervention was high with 85% of participants reporting satisfaction with the program overall. Self-management intervention providers delivered all required activities at 72% of visits. Providers also noted some challenges navigating the shared decision-making process and deciding what self-management tools to prioritize. Over the six-month study period, completion rates were 91% for monthly surveys and 86% for weekly surveys.ConclusionWe were able to demonstrate that a full-scale randomized trial comparing a whole-person supported self-management intervention to medical care for chronic BRLP is feasible and identified important areas for optimization.

A Q-Learning Based Target Coverage Algorithm for Wireless Sensor Networks

To address the problems of unclear node activation strategy and redundant feasible solutions in solving the target coverage of wireless sensor networks, a target coverage algorithm based on deep Q-learning is proposed to learn the scheduling strategy of nodes for wireless sensor networks. First, the algorithm abstracts the construction of feasible solutions into a Markov decision process, and the smart body selects the activated sensor nodes as discrete actions according to the network environment. Second, the reward function evaluates the merit of the smart body’s choice of actions in terms of the coverage capacity of the activated nodes and their residual energy. The simulation results show that the proposed algorithm intelligences are able to stabilize their gains after 2500 rounds of learning and training under the specific designed states, actions and reward mechanisms, corresponding to the convergence of the proposed algorithm. It can also be seen that the proposed algorithm is effective under different network sizes, and its network lifetime outperforms the three greedy algorithms, the maximum lifetime coverage algorithm and the self-adaptive learning automata algorithm. Moreover, this advantage becomes more and more obvious with the increase in network size, node sensing radius and carrying initial energy.

N-Bipolar Soft Expert Sets and Their Applications in Robust Multi-Attribute Group Decision-Making

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.

Decision-making process regarding safety and crisis management for childcare workers: Focusing on food allergy management

When it comes to childcare, parents want to rest assured that, in their absence, their children are being provided with engaging activities, balanced nutrition and safety. Awareness of food allergies is key in childcare settings but most childcare workers donâ–™t have medical backgrounds and therefore staff training on food allergies and related crises is imperative. Kayo Tamura is a former childcare worker and lecturer at Aichi Gakusen University in Japan who is developing guidelines for decision making processes for food allergy and crisis management for childcare workers. Guidelines were implemented by the Japanese Government following incidents involving allergies and school lunches in 1988 and 2012. However, Tamura is concerned that some teachers and childcare workers may be unable to implement these, meaning that accidents will continue to occur. Therefore, she is working with childcare providers to better understand how guidelines and training are received and implemented in childcare settings. In order to assess how much knowledge about food allergies and sense of crisis awareness students have developed by graduation, Tamara conducted a questionnaire survey of university students studying childcare. She also conducted a survey using the same questions, this time posed to childcare workers and performed a statistical analysis of the results. She found that many students already had some awareness of food allergies, often through personal experience but they acquired more specialist knowledge through university by reviewing government guidelines and manuals and through childcare training.

Diversity Barriers in Animal Care Careers at Zoos and Aquariums

We applied social cognitive career theory and systems theory to explore the barriers that historically excluded groups face when pursuing animal care careers at U.S. zoos and aquariums. Using a mixed-methods approach, this research combines survey data (N = 592) and interviews (N = 15) from zoo and aquarium animal care staff, including zookeepers, veterinarians, and aquarists. The findings show that historically excluded groups of animal caretakers report higher levels of perceived barriers to securing zoo positions compared to White animal caretakers, particularly in career awareness, self-efficacy, mentorship, and access to internship opportunities. Statistical analyses of animal caretakers revealed a statistically significant difference between historically excluded groups and their White colleagues. Historically excluded groups experience more challenges in securing and succeeding in animal care careers. While institutional barriers did not differ significantly between groups, this study underscores the need for zoos to address systemic inequities and create more inclusive environments. These findings are critical for zoos aiming to diversify their staff and enrich their decision-making processes.

Integrating Artificial Intelligence in agricultural higher education: Transforming learning and research

The integration of Artificial Intelligence (AI) in agricultural higher education is transforming the landscape of agricultural practices and research. This paper explores the multifaceted applications of AI technologies in the curriculum and pedagogical approaches of agricultural institutions. By enhancing data analysis, predictive modeling, and decision-making processes, AI empowers students and researchers to tackle complex agricultural challenges such as crop management, pest control, and resource optimization. Furthermore, the study examines the implications of AI on student engagement, skill development, and interdisciplinary collaboration. As agricultural sectors increasingly rely on data-driven solutions, the incorporation of AI in higher education not only prepares future professionals with essential competencies but also fosters innovation in sustainable agricultural practices. This article underscores the critical role of AI in shaping the future of agricultural education and its potential to revolutionize the industry.

Influence of Electronic Customer Relationship Management Strategy on Performance of Small and Medium Enterprises in Kenya

Despite the high cost of electronic customer relationship software solutions, its use can be justified as an investment in building long-term client relationships, improving customer retention, and, ultimately, boosting long-term profitability. As a result, it is critical for small- and medium-sized firms (SMEs) in Kenya to take a forward-thinking attitude and maintain their market survival by adapting to developing trends in customer relationship management. This is because the modern business setting has become exceedingly dynamic, necessitating small and medium enterprises to formulate strategies that align with the rapid evolution of technology, shifting customer demands, and intensified global competition to improve their performance. The findings indicate SMEs in Kenya are increasingly implementing relatively uncomplicated information and communication technologies to enhance their capabilities in customer communication and information management. This strategic move is aimed at gaining a competitive edge by leveraging electronic customer relationship management practices. Small- and medium-sized enterprises perceive the communication component of electronic customer relationship management as more manageable, yet encounter challenges in effectively incorporating client information into their decision-making processes. Overall, it can be observed that electronic customer relationship management implementation in SMEs is often characterized by an ad hoc approach rather than a strategic one. SMEs need to align electronic customer relationship initiatives with the overall strategic goals to enhance their overall performance.

Ethics and end-of-life in pediatric and neonatal ICUs: a systematic review of recommendations.

Working in neonatal intensive care units (NICUs) or pediatric intensive care units (PICUs) entails making difficult decisions about children at the end of their lives that raise significant ethical issues. This review identified the ethical content of the papers containing expert guidelines and recommendations in relation to end-of-life decision-making in NICUs and PICUs, by analyzing ethical positions and ethical principles behind them. Systematic search was limited to the period from 1990 to 2023 and encompassed 6 bibliographic databases (Medline, PubMed, CINAHL, APA PsycINFO, Web of Science Core Collection, and Scopus), grey literature sources and relevant reference lists. The international, national, or institutional papers providing expert guidelines and recommendations comprehensively addressing either withholding/withdrawing of life sustaining treatment, palliative care, and/or intentional life terminating actions in NICUs and PICUs were included in analysis. Also, only papers published in English language were considered. Papers that were not developed by intensive care expert communities and those that were either too narrow (e.g., dealing with specific issues or specific patient groups) or broad (e.g., addressing issues of interest on general and abstract level) were excluded. The search data were gathered and deduplicated, partly by Mendeley software. Titles and abstracts were screened by three independent reviewers, and full-text papers further reviewed and assessed for eligibility. Subsequently, data of interest were extracted, and qualitative analysis was performed. Initial search retrieved 6784 papers from bibliographic databases and 363 from other utilized sources. Titles and abstracts from 2827 papers were screened. 17 full texts were further assessed resulting in a total number of 9 papers (6 from bibliographic databases and 3 from other sources) which met the inclusion criteria and were included for analysis. The papers were published from 2001 to 2021. Four papers primarily focus on NICU setting, while five on PICU. A total of 38 ethical positions were identified and were grouped under 5 themes according to the content of the positions, relating to: patients, parents, medical team, decision-making and treatment options. A total of 12 ethical principles were mentioned in the papers. The principle of beneficence emerged as the most prominent one. It was explicitly mentioned in all included papers except one. This review has shown that papers containing guidelines and recommendations on end-of-life decision-making in the NICU and PICU promote similar stances. The ethical principle of beneficence is at the core of the decision-making process, and all decisions are made focusing on the child's best interests.

Antiblack Reliance on Last Resort Sanctions: A Theoretical Perspective on School Discipline

Abstract This article presents a new approach to understanding higher rates of punitive discipline utilized against Black students and in Black schools by combining the theoretical construct of antiblackness with Robert Emerson’s last resort sanctions to create the framework of Antiblackness in Last Resort Sanctions. This article is the first to apply last resort sanctions to the school setting and the first to incorporate antiblackness into the concept of last resorts. This new theoretical perspective is advanced to interpret the consistent findings of disproportionate discipline of Black students across school types; additionally, this perspective can be used to guide future research on racially disparate discipline. As a framework, Antiblackness in Last Resort Sanctions identifies the ways in which the disciplinary decision-making process of educators within schools as organizations is influenced by the antiblackness that is embedded within the U.S. school system. By including analysis of both antiblackness and discipline as an organizational process, this theoretical framework contributes to existing literature on disciplinary disparities by further exploring discipline as its own setting of antiblackness rather than simply a tool of antiblackness within the education system. In doing so, this article and framework discuss how the disciplinary decision-making process reflects the manifestation of antiblackness in both the structure and purpose of disciplinary interventions. This article concludes with an exploration of how Antiblackness in Last Resort Sanctions can be utilized to inform continuing research on the use of school discipline against Black students.