This chapter introduces First Nations approaches to health care that have relevance for the Australian and Aotearoa New Zealand contexts. It examines the historical influences that impacted the health and well-being of First Nations in these countries and considers the need for adopting First Nations approaches to health care practice such as cultural safety, cultural responsiveness and other cultural frameworks. Several of the principles for practice are transferrable to international First Nations communities as well as culturally and linguistically diverse populations.

Being given partial information about the outcome of an experiment forces us to revise the probabilities that we assign to events. We show that combining a reference probability space with revealed information suggests replacing the reference probability measure by another, called conditional measure. Consequently, the distributions of random variables also change to conditional distributions in presence of information, in general. The expectation of a random variable conditional upon an event coincides with the standard expectation of the variable provided that the reference probability measure is replaced by the conditional one; that is, provided that one uses the conditional distribution of the random variable instead. Next, we introduce the concept of conditional expectation with respect to a sigma-field. This is the random variable returning the best guess of the random variable given the provided information. We give the explicit form of the Radon–Nikodym derivative connecting the conditional measure to the reference one. We conclude the chapter by computing explicitly some conditional distributions of random variables and with an application to stock price models.

Panel data consist of multiple observations for each unit in the data. The units can be investors, firms, households, and so on. Panel datasets that allow us to follow these units over time provide intuitive understanding of the unit’s behavior. The panel-data analysis tends to be better at addressing the causality issues in research than cross-sectional data. This chapter provides a wide range of examples of panel-data techniques, with the main focus on linear panel-data models. It covers pooled OLS estimators, the fixed-effects model, least-squares dummy variable estimator, difference-in-differences model, between estimator, random-effects model, Hausman–Taylor random-effects IV method, and briefly the dynamic panel-data models. The chapter also reviews stationarity and the generalized method of moments (GMM) briefly. An application of linear panel-data models, as well as lab work and a mini case study, are provided at the end of the chapter.

The school nurse is a nurse who works in a range of education settings, across all age groups. While Australia does not have a formal national school health service, nurses have worked in schools for over a century. Today, they are employed in various independent schools, colleges and fragmented programs within government schools. There has been interest in recent years in growing the presence of nurses in Australian schools to facilitate access to health care for students from disadvantaged backgrounds.

The aim with regression analysis is to summarize the observed data and study how the response of a dependent variable varies as the values of the independent variable(s) change. There are many models that examine this relationship by obtaining the estimates of parameters in a regression model. The classical linear regression model (CLRM) is the basis of all the other models discussed in this book. This chapter discusses the CLRM in detail using the ordinary least squares (OLS) estimation method. The outcome of OLS can also be used as a benchmark in more advanced analysis. The focus is on the assumptions and applications of this technique, starting from a single-regression model with one independent variable and then covering multiple linear regression models with many independent variables. The chapter provides an application to the capital asset pricing model, lab work on the CLRM, and a mini case study.

A masters-level overview of the mathematical concepts needed to master the art of derivatives pricing, this textbook is a must-have for anyone considering a career in quantitative finance in industry or academia. Starting from the foundations of probability, the book allows students with limited technical background to build a solid knowledge base of the most important notions. It offers a unique compromise between intuition and mathematics, even when discussing abstract notions such as change of measure. Mathematical concepts are initially introduced using “toy” examples, before moving on to examples of finance cases, in both discrete and continuous time. Throughout, numerical applications and simulations illuminate the analytical results. The end-of-chapter exercises test students’ understanding, with solved exercises at the end of each part to aid self-study. Additional resources are available online, including slides, code, and an interactive app.

A masters-level overview of the mathematical concepts needed to master the art of derivatives pricing, this textbook is a must-have for anyone considering a career in quantitative finance in industry or academia. Starting from the foundations of probability, the book allows students with limited technical background to build a solid knowledge base of the most important notions. It offers a unique compromise between intuition and mathematics, even when discussing abstract notions such as change of measure. Mathematical concepts are initially introduced using “toy” examples, before moving on to examples of finance cases, in both discrete and continuous time. Throughout, numerical applications and simulations illuminate the analytical results. The end-of-chapter exercises test students’ understanding, with solved exercises at the end of each part to aid self-study. Additional resources are available online, including slides, code, and an interactive app.

The third industrial revolution saw the creation of computers and an increased use of technology in industry and households. We are now in the fourth industrial revolution: cyber, with advances in artificial intelligence, automation and the internet of things. The third and fourth revolutions have had a large impact on health care, shaping how health and social care are planned, managed and delivered, as well as supporting wellness and the promotion of health. This growth has seen the advent of the discipline of health informatics with several sub-specialty areas emerging over the past two decades. Informatics is used across primary care, allied health, community care and dentistry, with technology supporting the primary health care continuum. This chapter explores the development of health informatics as a discipline and how health care innovation, technology, governance and the workforce are supporting digital health transformation.

The random walk is perhaps the simplest stochastic process one can think of. It is discrete time in the sense that it is defined at positive integer times only. We give the main features of this process, including its expectation and variance at any time, and establish the link between the probabilities of the values it can take at different times and the binomial distribution. Finally, we study a few transformations of the latter, including deterministic shifting or scaling. Taking the exponential of this linear transform yields the geometric random walk. Finally, we discuss how to change the time-step in order to obtain a process that is defined not only at integer times, but on every point of a given discrete-time grid. In all cases, we illustrate how the sample paths of the resulting process change. This stochastic process plays a central role in finance and is at the heart of the Cox–Ross–Rubinstein model.

A masters-level overview of the mathematical concepts needed to master the art of derivatives pricing, this textbook is a must-have for anyone considering a career in quantitative finance in industry or academia. Starting from the foundations of probability, the book allows students with limited technical background to build a solid knowledge base of the most important notions. It offers a unique compromise between intuition and mathematics, even when discussing abstract notions such as change of measure. Mathematical concepts are initially introduced using “toy” examples, before moving on to examples of finance cases, in both discrete and continuous time. Throughout, numerical applications and simulations illuminate the analytical results. The end-of-chapter exercises test students’ understanding, with solved exercises at the end of each part to aid self-study. Additional resources are available online, including slides, code, and an interactive app.

Despite current and predicted ongoing primary health care (PHC) nursing workforce shortages, the undergraduate nursing curricula in Australasia and internationally remain largely directed towards acute care. Additionally, the efforts of schools of nursing in supporting the career development of new graduate nurses and their transition to practice also remain largely focused on employment in acute care tertiary settings. Registered nurses are integral members of the multidisciplinary PHC team and fulfil various roles. These roles include managing acute presentations, coordinating care for people with complex chronic conditions, providing preventive care, promoting the health of individuals and communities, and supporting end-of-life care.

We introduce the concepts of sample space, sigma-field, and probability measure, which are the three components defining a probability space. We explain that, in general, many probability measures can be associated to a given sample space; which one to pick depends on the problem. Similarly, the list of events for which the probability can be computed in a given problem is the smallest sigma-field built from the events for which the probability is known from the problem. The discrete sigma-field corresponds to the special case where the information provided is substantial enough to yield the probability of every event. This establishes the connection between the concept of information and sigma-field, and shows that the latter is the appropriate structure to serve as definition domain of probability measures. We conclude the chapter with the concept of independence between events and between sigma-fields. Those concepts are illustrated on various examples featuring coins and colored dice. We conclude the chapter by proposing a first model to describe future stock prices.

ML methods are increasingly being used in (corporate) finance studies, with impressive applications. ML methods can be applied with the aim of reducing prediction error in the models, but can also be used to extend the existing traditional econometric methods. The performance of the ML models depends on the quality of the input data and the choice of model. There are many ML models, but all come with their own specific details. It is therefore essential to select accurate model(s) for the analysis. This chapter briefly reviews some broad types of ML methods. It covers supervised learning, which tends to achieve superior prediction performance by using more flexible functional forms than OLS in the prediction model. It explains unsupervised learning methods that derive and learn structural information from conventional data. Finally, the chapter also discusses some limitations and drawbacks of ML, as well as potential remedies.