This chapter describes the strengths and limitations of the methods that clinical scientists have used to determine if and how those various therapies work. These include box score reviews, within-subjects designs, between-subjects designs, and randomized controlled trials. The chapter contains a review of the latest research findings on the efficacy and effectiveness of established treatments as well as of alternative modes of intervention, with emphasis on the need to answer the “ultimate question” about psychotherapy, namely what treatment, by whom, is most effective for this individual with that specific problem, under which set of circumstances, and how does it come about? There is also a discussion about the internal and external validity of psychotherapy outcome research and how these factors affect clinical practice. The chapter concludes with a discussion of research on the effects of using psychoactive drugs, with or without psychotherapy, in the treatment of psychological disorders.

The idea that serves as a basis for research may be derived from any of several sources, including curiosity about a specific phenomenon, case studies, interest in special populations (e.g., those who meet criteria for a given disorder, those with a special history or experience), extrapolation of findings from other types of research (e.g., processes studied in nonhuman animal research), development of measurements, and many others. Also, research may seek to illuminate how variables or characteristics are related to each other as reflected in such concepts as correlates, risk factors, protective factors, and causes. The goal of research is to understand the phenomenon of interest and theory can help enormously. Multiple types of designs were considered including true experiments, quasi-experiments, observational studies, and single-case experimental designs. The challenge is to use the best design available to test the hypotheses and within any constraints such as working in a specific setting (hospital, school).

In the years following FDA approval of direct-to-consumer, genetic-health-risk/DTCGHR testing, millions of people in the US have sent their DNA to companies to receive personal genome health risk information without physician or other learned medical professional involvement. In Personal Genome Medicine, Michael J. Malinowski examines the ethical, legal, and social implications of this development. Drawing from the past and present of medicine in the US, Malinowski applies law, policy, public and private sector practices, and governing norms to analyze the commercial personal genome sequencing and testing sectors and to assess their impact on the future of US medicine. Written in relatable and accessible language, the book also proposes regulatory reforms for government and medical professionals that will enable technological advancements while maintaining personal and public health standards.

This chapter explores the evidence base that informed the work of donors. It examines the analytical work undertaken in developing countries, mostly financed by multilateral and bilateral agencies. Three trajectories in this body of education research can be identified: (i) large-scale surveys showing broad trends and patterns; (ii) randomized controlled trials claiming to show “what works” in education; and (iii) political economy research. I highlight the strengths and limitations of each of these research tracks, examining to what extent studies were able to inform project design or steer mid-course correction to improve learning. The discussion shows how the predominance of one approach blinds practitioners to the specificities and complexities of the two levels (micro and meso) critical for learning. This also displaces much-needed resources for knowledge generation in the areas that impact learning. In sum, the educational ecosystem in a country, which includes numerous institutions at the micro and meso levels interacting and working together to make a school system function, is a black box.

The use of antiepileptic drugs (AEDs) is effective in reducing the risk of developing early (acute symptomatic) post-traumatic seizures compared to placebo or usual care in patients with severe TBI (low-quality evidence). With regards to the choice of the AED, the available evidence supports the use of phenytoin, starting with an intravenous loading dose initiated as soon as possible after severe TBI]. Despite the lack of evidence from comparative clinical trials, levetiracetam is increasingly used in primary prevention of early post-traumatic seizures due to its ease of use, favorable safety profile and lack of pharmacokinetic interactions. So far, there is no evidence to support the use of other neuroprotective agents for the primary prevention of early post-traumatic seizures. Patients with early post-traumatic seizures do not generally require long-term AED treatment since their risk to develop post-traumatic epilepsy is low. High-quality and adequately powered trials conducted in a selected population at high risk of developing late post-traumatic seizures are required to draw definite conclusions on the effectiveness of long-term prophylactic treatment. Further studies to explore the antiepileptogenic and neuroprotective effects of anti-inflammatory and immune-modulatory therapies are also warranted.

The term natural experiment is used inconsistently. In one interpretation, it refers to an experiment where a treatment is randomly assigned by someone other than the researcher. In another interpretation, it refers to a study in which there is no controlled random assignment, but treatment is assigned by some external factor in a way that loosely resembles a randomized experiment–often described as an "as if random" assignment. In yet another interpretation, it refers to any non-randomized study that compares a treatment to a control group, without any specic requirements on how the treatment is assigned. I introduce an alternative definition that seeks to clarify the integral features of natural experiments and at the same time distinguish them from randomized controlled experiments. I define a natural experiment as a research study where the treatment assignment mechanism (i) is neither designed nor implemented by the researcher, (ii) is unknown to the researcher, and (iii) is probabilistic by virtue of depending on an external factor. The main message of this definition is that the difference between a randomized controlled experiment and a natural experiment is not a matter of degree, but of essence, and thus conceptualizing a natural experiment as a research design akin to a randomized experiment is neither rigorous nor a useful guide to empirical analysis. Using my alternative definition, I discuss how a natural experiment differs from a traditional observational study, and other practical recommendations for researchers who wish to use natural experiments to study causal effects.

Experiments have come to be a widely accepted and highly regarded method for political science research. Randomization allows for well identified causal effects that are “internally valid” to the experimental setting. However, political scientists are driven by asking big questions with broad impacts, and we are rarely able to conduct the ideal experiment on a representative sample of the target population of interest. This leaves open the question: under what conditions can we make inferences about the effectiveness of treatment for a different population, one upon which we did not conduct our experiment. This problem of “external validity” has seen increased attention in statistics and the broader social sciences as researchers and policy makers wish to know the impact of interventions beyond the experimental sample. This chapter examines the assumptions researchers must make in order to generalize their experimental findings and the implications for how researchers should design experiments if they wish to maximize the generalizability of their findings.

There are many expert-identified recommended interventions to improve medication safety: few have been rigorously tested and proven. Adoption of electronic medication processes can and has reduced medication error on the wards and in the OR. More recently, comprehensive patient safety programs have been shown to reduce medication errorsaw well as mortality. Reduction of human error in medicine will require a comprehensive bundle of interventions rather than any single silver bullet.There are many things that most institutions and practitioners could do today: each of these may make only a small difference but the key to substantially improving safety lies in the aggregation of minimal gains. Our patients have a right to expect greater investment into medication safety by health care institutions, and greater engagement with medication safety by the clinicians who care for them. Although their time in the OR is only part of the surgical patient's perioperative journey, it is an important part. Implementation of these recommendations should be a minimum expectation for institutions and anesthesia departments today, and is an excellent foundation from which initiatives to improve medication safety can be extended to the rest of the surgical pathway.

Rigorous evaluation of interventions is vital to advance the science of behavior change and identify effective interventions. Although randomized controlled trials (RCTs) are often considered the “gold standard”, other designs are also useful. Considerations when choosing intervention design are the research questions, the stage of evaluation, and different evaluation perspectives. Approaches to explore the utility of an intervention, include a focus on (1) efficacy; (2) “real-world” effectiveness; (3) how an intervention works to produce change; or (4) how the intervention interacts with context. Many evaluation designs are available: experimental, quasi-experimental, and nonexperimental. Each has strengths and limitations and choice of design should be driven by the research question. Choosing relevant outcomes is an important step in planning an evaluation. A typical approach is to identify one primary outcome and a narrow range of secondary outcomes. However, focus on one primary outcome means other important changes may be missed. A well-developed program theory helps identify a relevant outcomes. High-quality evaluation requires (1) involvement of relevant stakeholders; (2) evaluating and updating program theory; (3) consideration of the wider context; (4) addressing implementation issues; and (5) appropriate economics input. Addressing these can increase the quality, usefulness, and impact of behavior change interventions.