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The chapter considers the change of position of the Home Office on the value of randomised controlled trials (RCTs) in England and Wales which took place around 2003 after the end of the Crime Reduction Programme (CRP). Before the CRP Home Office researchers had shown little interest in RCTs; after it, they came close to arguing that no other kinds of evaluation research were worth doing. This represented a reversal of a position that had dominated Home Office thinking on the issue for almost 30 years – that RCTs were in general impractical and unlikely to produce clear-cut results. This view was based in part on the experience of RCTs in the 1970s, which led influential researchers to conclude that the method could not be transferred from medicine to criminal justice. But, disappointed with the lack of definite results from the CRP, the Home Office turned back to RCTs as a potential source of certainty about what works. The chapter considers two recent scholarly exchanges on the question, in relation to an evaluation of a community crime reduction programme, for which an experimental design was attempted but not achieved, and to Lawrence Sherman's recent advocacy of RCTs and his use of research on restorative justice as an example of the successful use of the method. The chapter argues that the restorative justice research, while of very high quality, does not provide as clear an example of the use of an RCT as Sherman claims, and concludes with some reflections on the inherent difficulties of criminal justice evaluation, and on the lack of a predictable, rational relationship between research quality and policy influence.

In this chapter, we examined issues related to research design and research management as applied to scientific research conducted in applied school settings. In terms of research design, randomized controlled trials (RCTs) have an important role to play in advancing a scientific agenda for school-based research. However, given their enormous cost and complexity, it is important to carefully time their implementation in the development cycle. We suggested that the use of RCTs is most appropriate in the later stages of the development cycle when the focus is on demonstrating the efficacy and/or effectiveness of an intervention and establishing its generalizability under the real world conditions of schooling. We also recommended establishing a hierarchy of evidence for an intervention that involves implementing a cost-efficient mix of single case, quasi-experimental, and true experimental designs where appropriate and feasible. In examining issues related to the management of research and the implementation of a knowledge development agenda for schools, it has become apparent that treatment integrity is a keystone variable. We discussed the importance of treatment integrity, with attention to the impact on internal and external validity. Finally, we offered practical considerations to support high-quality, respectful school-based inquiry.

Though we have recently witnessed the “exponential production of digital data to measure, analyze, and predict educational performance” (Salajan & Jules, this volume), there has not been sufficient attention given to the quantitative methods that are used to process and transform this data in order to arrive at findings related to “what works”. This chapter addresses this gap by discussing a range of constraints that affect the main methods used for this purpose, with these methods being known as “impact evaluation.” Specifically, this chapter addresses its purpose, first, by making explicit the methodological assumptions, technical weaknesses, and practical shortcomings of the two main forms of impact evaluation—regression analysis and randomized controlled trials. Although the idea of Big Data and the ability to process it is receiving more attention, the underlying point here is that these new initiatives and advances in data collection are still dependent on methods that have serious limitations. To that end, not only do proponents of Big Data avoid or downplay discussion of the methodological pitfalls of impact evaluation, they also fail to acknowledge the political and organizational dynamics that affect the collection of data. To the extent that such methods will increasingly be used to guide public policy around the globe, it is essential that stakeholders inside and outside education systems are informed about their weaknesses—methodologically and in terms of their inability to take the politics out of policymaking. While the promises of Big Data are seductive, they have not replaced the human element of decision making.

The use of behavioral insights and experimental methods has recently gained momentum among health policy-makers. There is a tendency, however, to reduce behavioral insights applications in health to “nudges,” and to reduce experiments in health to “randomized controlled trials” (RCTs). We argue that there is much more to behavioral insights and experimental methods in health economics than just nudges and RCTs. First, there is a broad and rich array of complementary experimental methods spanning the lab to the field, and all of them could prove useful in health economics. Second, there are a host of challenges in health economics, policy, and management where the application of behavioral insights and experimental methods is timely and highly promising. We illustrate this point by describing applications of experimental methods and behavioral insights to one specific topic of fundamental relevance for health research and policy: the experimental elicitation and econometric estimation of risk and time preferences. We start by reviewing the main methods of measuring risk and time preferences in health. We then focus on the “behavioral econometrics” approach to jointly elicit and estimate risk and time preferences, and we illustrate its state-of-the-art applications to health.

Education researchers have been urged to utilize causal inference methods to estimate the policy effect more rigorously. While randomized controlled trials (RCTs) are the gold standard for assessing causality, RCTs are infeasible in some educational settings, particularly when ethical concerns or high cost are involved. Quasi-experimental research designs are the best alternative approach to study educational topics not amenable to RCTs, as they mimic experimental conditions and use statistical techniques to reduce bias from variables omitted in the empirical models. In this chapter, we introduce and discuss the core concepts, applicability, and limitations of three quasi-experimental methods in higher education research (i.e., difference-in-differences, instrumental variables, and regression discontinuity). By introducing each of these techniques, we aim to expand the higher education researcher's toolbox and encourage the use of these quasi-experimental methods to evaluate educational interventions.

Using the pharmaceutical sector as a microcosm of the health sector, we highlight the most prevalent structural and policy issues that make this sector susceptible to corruption and ways in which these vulnerabilities can be addressed. We conducted a literature review of publications from 2004 to 2015 that included books, peer-reviewed literature, as well as gray literature such as working papers, reports published by international organizations and donor agencies, and newspaper articles discussing this topic. We found that vulnerabilities to corruption in the pharmaceutical sector occur due to a lack of good governance, accountability, transparency, and proper oversight in each of the decision points of the pharmaceutical supply chain. What works best to limit corruption is context specific and linked to the complexity of the sector. At a global level, tackling corruption involves hard and soft international laws and the creation of international standards and guidelines for national governments and the pharmaceutical industry. At a national level, including civil society in decision-making and monitoring is also often cited as a positive mechanism against corruption. Anticorruption measures tend to be specific to the particular “site” of the pharmaceutical system and include improving institutional checks and balances like stronger and better implemented regulations and better oversight and protection for “whistle blowers,” financial incentives to refrain from engaging in corrupt behavior, and increasing the use of technology in processes to minimize human discretion. This chapter was adapted from a discussion piece published by Transparency International UK entitled Corruption in the Pharmaceutical Sector: Diagnosing the Challenges.

Psychotherapy is perhaps the most known and identifiable with one of the field’s originators Sigmond Freud who is often accredited as being the inventor of the talking cure. However, it was many decades after psychoanalysis was first used by Freud that robust research and evidence was applied to psychotherapy, and its findings used to support practice in various contexts. While psychoanalysis is still practiced, the field has moved on and includes many hundreds if not thousands of approaches to healing. What has not improved, in general, is the between school rivalry regarding the efficacy and effectiveness of the different approaches. While it is now accepted that in general terms all approaches are about equally effective, certain research is often provided with more legitimacy than others. Mainly, the randomised control trial (RCT) is considered the gold standard in research terms, especially when it comes to establishing the efficacy and effectiveness of different psychotherapies. Empirically supported treatments (ESTs) are in prime position to take advantage of these political decisions, and this chapter will introduce the reader to these debates.

Introduction: Coronavirus-19 (COVID-19) global outbreak poses a danger to millions of people’s health and the uncertainty and financial prudence around the world. Without a doubt, the sickness will place a tremendous strain on healthcare systems, which existing or traditional-based treatments cannot adequately handle. Only intelligence derived from diverse data sources can provide the foundation for rigorous clinical and social responses that optimise the use of constrained healthcare resources, create tailored patient treatment plans, educate policy-makers, and accelerate clinical trials

Purpose: This chapter aims to incorporate innovative practices of artificial intelligence (AI) into local, national, and global healthcare systems that can save lives of people and as well helps in human capital management ways that may be deployed rapidly and effectively with minimal errors.

Methodology: AI technologies and tools play a crucial part in COVID-19 crisis response by assisting with the virus discovery, early detection, and the development of effective medications and therapies. In this chapter, significant issues related to COVID-19 and how they may be addressed by applying HRM practices with recent advances in AI. Also, through a literature review of the recent studies implemented in a similar context, an AI solution is proposed by formulating a conceptual model.

Findings: This chapter offers that the latest AI techniques can assist policy-makers in implementing modern human capital management practices to fight against COVID-19. The goal is to remotely monitor patients utilising gadgets that are embedded with state-of-the-art medical technology. To limit hospital visits, or at least cut them down to a minimum, on the one hand, the health clinic also wants to deliver reliable health information to the doctors before or during virtual consultations.

This paper explores through Schumacher's perspective on ‘the proper use of land’: the reasons for, and the means and consequences of, monitoring soil condition in managing agricultural landscapes sustainably. This particular perspective illustrates its argument with soil monitoring initiatives operating at various scales within the global agricultural context. Schumacher's land management goals are health, beauty and permanence, yet productivity is the goal most land managers focus on. The chosen indicators for soil monitoring need to reflect these goals. Hence, the indicators of choice for monitoring soil condition are attributes that can be: easily measured, improve soil productivity or protect the soil. Often attributes that have intrinsic ‘beauty’ (value), maintain ‘health’ (function) in ecosystems and are difficult to measure are ignored as soil condition indicators. The usefulness of information gained through monitoring soil condition is to make decisions that will be relevant for varied audiences and at different points in the decision-making process.