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This chapter aims to provide an overview of the ethical framework and decision-making in clinical dementia research, and to analyze and discuss the ethical challenges and issues that can arise when conducting clinical dementia research.

Informed consent is the most scrutinized and controversial aspect of clinical research ethics. In clinical dementia research, assessing decision-making capacity may be challenging as the nature and progress of each disease influences decision-making capacity in diverse ways. Persons with dementia represent a vulnerable population deserving special attention when developing, implementing, and evaluating the informed consent process. In this chapter, particular attention will be given to vulnerability categories and how these influence decision-making capacity. Ethical frameworks with a pragmatic contour and implication are needed to protect vulnerable patients from potential harms and ensure their optimal participation in clinical dementia research.

In addition, this chapter analyses important ethical challenges and issues in clinical dementia research. If handled thoughtfully, they would not pose insuperable barriers to research. But if they are ignored, they could slow the research process, alienate potential study subjects and cause harm to research participants. Ethical considerations in research involving persons with dementia primarily concern the representation of the interests of the participants with dementia and protection of their vulnerabilities and rights.

A core set of ethical questions and recommendations are drawn to aid researchers, institutional review boards and potential research participants in the process of participating in clinical dementia research.

Procedures can be categorized as certain surgeries based on their necessity and outcomes while others are classified as uncertain surgeries based on these areas. To account for this variance, policies such as the Affordable Care Act (ACA) call for health care providers to engage in shared decision making (SDM) with patients to ensure they are informed of treatment options and asked their preferences. Yet, gender may influence the decision-making process. Thus, this project examines the decision process and how gender impacts patients’ participation in decisions to undergo certain surgeries compared to uncertain surgeries.

This research project analyzed data from the National Survey of Medical Decisions 2006–2007 which surveyed the medical decisions of US residents 40 and older.

First, the data reveals that women felt more informed having uncertain surgeries compared to men. Second, patients were less likely asked their preference for surgery when undergoing certain surgeries compared to uncertain surgeries. Third, compared to men, women having uncertain surgeries were less likely to make the final decision to have surgery, compared to sharing the final decision with health care providers.

Due to the sample size, this project could not perform three-way interactions between gender, race, and surgery type.

Gender influences the level patients feel informed having uncertain surgeries. Though policy calls for SDM, health care providers are less likely to ask patients their preference for surgery regarding certain surgeries, relative to uncertain surgeries. Gender impacts the final decision-making process regarding whether patients should have uncertain surgeries.

Artificial intelligence (AI) refers to a type of algorithms or computerized systems that resemble human mental processes of decision-making. This position paper looks beyond the sensational hyperbole of AI in teaching and learning. Instead, this paper aims to explore the role of AI in educational leadership.

To explore the role of AI in educational leadership, I synthesized the literature that intersects AI, decision-making, and educational leadership from multiple disciplines such as computer science, educational leadership, administrative science, judgment and decision-making and neuroscience. Grounded in the intellectual interrelationships between AI and educational leadership since the 1950s, this paper starts with conceptualizing decision-making, including both individual decision-making and organizational decision-making, as the foundation of educational leadership. Next, I elaborated on the symbiotic role of human-AI decision-making.

With its efficiency in collecting, processing, analyzing data and providing real-time or near real-time results, AI can bring in analytical efficiency to assist educational leaders in making data-driven, evidence-informed decisions. However, AI-assisted data-driven decision-making may run against value-based moral decision-making. Taken together, both leaders' individual decision-making and organizational decision-making are best handled by using a blend of data-driven, evidence-informed decision-making and value-based moral decision-making. AI can function as an extended brain in making data-driven, evidence-informed decisions. The shortcomings of AI-assisted data-driven decision-making can be overcome by human judgment guided by moral values.

The paper concludes with two recommendations for educational leadership practitioners' decision-making and future scholarly inquiry: keeping a watchful eye on biases and minding ethically-compromised decisions.

This paper brings together two fields of educational leadership and AI that have been growing up together since the 1950s and mostly growing apart till the late 2010s. To explore the role of AI in educational leadership, this paper starts with the foundation of leadership—decision-making, both leaders' individual decisions and collective organizational decisions. The paper then synthesizes the literature that intersects AI, decision-making and educational leadership from multiple disciplines to delineate the role of AI in educational leadership.

This paper aims to investigate the effect of cancer patients’ information behaviour on their decision-making at the diagnosis and treatment stages of their cancer journey. Patients’ information sources and their decision-making approaches were analyzed.

Semi-structured interviews were conducted with 15 participants.

The cancer patients sought information from various sources in choosing a hospital, physician, treatment method, diet and alternative therapy. Physicians were the primary information source. The patients’ approaches to treatment decision-making were diverse. An informed approach was adopted by nine patients, a paternalistic approach by four and a shared decision-making approach by only two.

In practice, the findings may assist hospitals and medical professionals in fostering pertinent interactions with patients.

The findings can enhance researcher understanding regarding the effect of cancer patients’ information behaviour on their decision-making.

The purpose of this paper is to change the traditional supplier selection methods by shifting the emphasis from using a single model to using multiple models in the unstructured decision‐making context and to provide a tool for decision makers to make informed decisions of supplier selection in the multiple perspectives.

There are various supplier selection models available in the literature. However, using the result of a single model as a basis for making the final decision could lead to a biased decision given the fact that any model has its limitations. The qualities of the decision‐making process and the decision itself increase by applying a multiple perspectives approach rather than a single model. The multiple perspectives decision‐making allows collaboration and knowledge sharing among the participants which leads to a less‐biased decision. This study examines commonly applied supplier selection models, formulates general perspectives of these models, and proposes a framework of multiple perspectives decision making for supplier selection. It further provides a structure of supplier selection system based on the proposed approach. Through a prototype of web portal, the study demonstrates the usefulness of the proposed multiple perspective system approach in the decision context of collaboration and knowledge sharing.

The general finding from this study is that the multiple perspectives approach to supplier selection enables the decision makers to actively participate and fully understand the decision‐making process through knowledge sharing which in turn ensures high quality of the final decisions.

Supplier selection decision makers can make more informed decisions through collaboration among all decision‐making participants in the multiple perspectives. It informs supply chain managers of the potentially positive effect of knowledge sharing on the decision‐making process in supplier selection.

Multiple perspectives decision making provides a novel approach that emphasizes on the knowledge sharing and collaboration between the experts, who are familiar with the supplier relations, and the decision makers who are responsible for the final decisions.

The purpose of this article is to discuss approaches to sustainable decision-making for integrating emerging educational technologies in library instruction while supporting evidence-based practice (EBP).

This article highlights recent trends in emerging educational technologies and EBP and details a model for supporting evidence informed decision-making. This viewpoint article draws on an analysis of recent literature, as well as experience from professional practice.

Authors discuss the need for sustainable decision-making that addresses a perceived lack of evidence surrounding emerging technologies, a dilemma that many library educators and practitioner-researchers will have faced in their own library instruction. To support the evidence-informed selection and integration of emerging educational technologies, a two-pronged model is presented, beginning with an articulation of pedagogical aims, alignment of technological affordances to these aims and support of this alignment via hard evidence available in the research literature, as well as soft evidence found in the environmental scan.

This article provides an outline and synthesis of key issues of relevance to library practitioners working within a challenging and ever-changing landscape of technologies available for learning and instruction. The proposed approach aims to create a sustainable model for addressing problems of evidence and will benefit academic librarians considering emerging educational technologies in their own pedagogy, as well as those who support the pedagogy of others.

Program budgeting and marginal analysis (PBMA) is a priority setting approach that assists decision makers with allocating resources. Previous PBMA work establishes its efficacy and indicates that contextual factors complicate priority setting, which can hamper PBMA effectiveness. The purpose of this paper is to gain qualitative insight into PBMA effectiveness.

A Canadian case study of PBMA implementation. Data consist of decision-maker interviews pre (n=20), post year-1 (n=12) and post year-2 (n=9) of PBMA to examine perceptions of baseline priority setting practice vis-à-vis desired practice, and perceptions of PBMA usability and acceptability.

Fit emerged as a key theme in determining PBMA effectiveness. Fit herein refers to being of suitable quality and form to meet the intended purposes and needs of the end-users, and includes desirability, acceptability, and usability dimensions. Results confirm decision-maker desire for rational approaches like PBMA. However, most participants indicated that the timing of the exercise and the form in which PBMA was applied were not well-suited for this case study. Participant acceptance of and buy-in to PBMA changed during the study: a leadership change, limited organizational commitment, and concerns with organizational capacity were key barriers to PBMA adoption and thereby effectiveness.

These findings suggest that a potential way-forward includes adding a contextual readiness/capacity assessment stage to PBMA, recognizing organizational complexity, and considering incremental adoption of PBMA’s approach.

These insights help us to better understand and work with priority setting conditions to advance evidence-informed decision making.

Reflecting the Mauritian government's “quality” agenda and its focus on school leadership, this paper reports the findings of research exploring Mauritian principals’ views about the use of total quality management (TQM) for school improvement. While aspects of this research have been reported elsewhere, the purpose of this paper is to focus on school leaders’ use of data and evidence in making decisions for school improvement.

The paper reports on qualitative aspects within a mixed methods research with data collected by means of semi-structured interviews conducted with a purposive sample of six principals. The analysis of the data were an exercise in grounded theory building.

The paper expands the knowledge of principals as quantitative data users arguing that qualitative information based on professional discourses, human judgements and lived experiences should be equally valorised if TQM is used for making informed educational decisions.

The research relied on principals’ views as the unique source of data. The perspectives of the other stakeholders would offer a richer description of leadership reality in Mauritian schools.

The paper suggests a more participatory decision-making model for effective change that could rightfully engage all stakeholders through various complementary quantitative and qualitative processes. It further recommends that alongside the core systemic qualities of TQM, there are ethical, moral and cultural dimensions of leadership that could enhance the teaching and learning environment.

While confirming some extant research, the paper brings new thinking to understanding the critical role of principals within the TQM scenario of data-driven decision making.

The purpose of this study is to examine the rostering practices and work experiences of medical scientists at four health services in the Australian public healthcare sector. There are over 16,000 medical scientists (AIHW, 2019) in Australia responsible for carrying out pathology testing to help save the lives of thousands of patients every day. However, there are systemic shortages of medical scientists largely due to erratic rostering practices and workload issues. The purpose of this paper is to integrate evidence-based human resource management (EBHRM), the LAMP model and HR analytics to enhance line manager decision-making on rostering to support the wellbeing of medical scientists.

Using a qualitative methodological approach, the authors conducted 21 semi-structured interviews with managers/directors and nine focus groups with 53 medical scientists, making a total 74 participants from four large public hospitals in Australia.

Across four health services, manual systems of rostering and management decisions do not meet the requirements of the enterprise agreement (EA) and impact negatively on the wellbeing of medical scientists in pathology services. The authors found no evidence of the systematic approach of the organisations and line managers to implement the LAMP model to understand the root causes of rostering challenges and negative impact on employees. Moreover, there was no evidence of sophisticated use of HR analytics or EBHRM to support line managers' decision-making regarding mitigation of rostering related challenges such as absenteeism and employee turnover.

The authors contribute to HRM theory by integrating EBHRM, the LAMP model (Boudreau and Ramstad, 2007) and HR analytics to inform line management decision-making. The authors advance understandings of how EBHRM incorporating the LAMP model and HR analytics can provide a systematic and robust process for line managers to make informed decisions underpinned by data.

Enhancing the resilience of cities and strengthening risk-informed decision-making are defined as key within the Global Agenda 2030. Implementing risk-informed decision-making also requires the consideration of scenarios of exposure and vulnerability. Therefore, the paper presents selected scenario approaches and illustrates how such vulnerability scenarios can look like for specific indicators and how they can inform decision-making, particularly in the context of urban planning.

The research study uses the example of heat stress in Ludwigsburg, Germany, and adopts participatory and quantitative forecasting methods to develop scenarios for human vulnerability and exposure to heat stress.

The paper indicates that considering changes in future vulnerability of people is important to provide an appropriate information base for enhancing urban resilience through risk-informed urban planning. This can help cities to define priority areas for future urban development and to consider the socio-economic and demographic composition in their strategies.

The value of the research study lies in implementing new qualitative and quantitative scenario approaches for human exposure and vulnerability to strengthen risk-informed decision-making.