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.

The role of occupational health nurses is to improve mental and physical health outcomes and the well-being of workers. These benefits can often extend to family and community. Workers’ health is impacted by several factors including fatigue, gender, culture, age, language, living conditions, access to nutritious food, level of physical activity, sleep patterns, personal health practices and coping strategies, levels of social support and inclusion, personal safety and freedom from violence.

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.

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 evolution of the value of a trading strategy in discrete time is a weighted sum of quantities scaled by the change in the assets value. The limit as the time-step tends to zero yields a continuous-time process taking the form of a sum of integrals of a process (number of shares) with respect to another (assets price). This is a stochastic integral. Having recalled well-known Riemann integrals, we introduce the Riemann–Stieltjes extension and recall that their value can be estimated arbitrarily well using a finite sum. We introduce the differential of a process, and of a stochastic integral in particular. An Itô integral is a special case of stochastic integrals where a process is integrated with respect to a Brownian motion. In general, the analytical expression of a stochastic integral does not coincide with the expression suggested by Riemann–Stieltjes integrals because the paths of a Brownian motion are not differentiable. This gap is illustrated numerically on a simple example, comparing the tentative explicit solution with the numerical estimation found using the discrete sum approximation. We conclude the chapter by providing the main properties of the Itô integral.

Nurses work in a wide variety of settings, and this includes a wide variety of communities. In Australia and Aotearoa New Zealand, many of these communities are rural and require nurses to have a broad general range of skills to meet the diversity of needs that their clients present with. Rural health nurses may be sole practitioners, providing health care on their own, or as part of a small team that sometimes may include doctors. An increased scope of practice and greater reliance on collaboration, interdisciplinary and transdisciplinary practice is common.

Nurse practitioners (NPs) are a valued addition to the primary health care (PHC) team and are well-placed to increase accessibility to quality health care services while offering consumer choice. NPs have undertaken advanced education and clinical training. In addition, they have demonstrated their competency, capacity and capability to provide high-quality, effective and efficient clinically focused health care delivery. Although many NPs practice in rural or underserviced communities, NPs practice across a diverse range of health care settings, delivering either specialist or generalist health services. Recognised as advanced practice nurses internationally and nationally, the NP role has emerged as a response to meet the challenges of rising health care demand and is proving effective in promoting transformational changes within the PHC sector.

We introduce derivative securities and ask ourselves how to determine their price from a financial perspective. We discover that the cashflow of zero-coupon bonds and forward contracts can be artificially replicated by adopting a static trading strategy featuring primary assets. With almost no math, we obtain the central result that every product whose payoff is a linear function of the future price of tradeable products can be computed without relying on any model. The story is different for products whose payoff is a non-linear function of the future price of assets, such as European calls and puts. In such cases, pricing by replication may still be possible, but is more complex but it requires a model and features a dynamic replicating strategy, evolving through time. We use the law of one price to give a clear interpretation to the no-arbitrage price of derivatives. We conclude the chapter with the general expression of a derivative’s price, given by the risk-neutral expectation of its payoff discounted at the risk-free rate. The purpose of the book is to introduce all the concepts needed to understand why and when this result holds, and how it can be evaluated in practice.