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Background: A raft of initiatives and reforms have been introduced in many countries to attract and recruit school teachers, many of which do not have a clear evidence base, so their effectiveness remains unclear. Prior research has been largely correlational in design. This paper describes a rigorous and comprehensive review of international evidence, synthesising the findings of some of the strongest empirical work so far. Methods: The review synthesises a total of 120 pieces of research from 13 electronic databases, Google/Google scholar and other sources. Each study is weighted by strength of evidence. Results: The strongest evidence suggests that targeted money can encourage people into teaching but does not necessarily keep them in the teaching profession. The money needs to be large enough to compensate for the disadvantages of working in certain schools and areas, and competitive enough to offset the opportunity costs of not being in more lucrative occupations, and its effect is only short-term. Conclusions: Continuing professional development (CPD) and early career support could be promising approaches for retaining teachers in the profession, but the evidence for them is weak. There is no evidence that any other approaches work, largely because of the lack of robust studies.

People seek the best in every aspect of life. However, little is known about the neurobiological mechanisms supporting this process of maximization. In this study, maximization tendencies were increased by using high-definition transcranial direct current stimulation (HD-tDCS) over the right dorsolateral prefrontal cortex (DLPFC). Participants ( n = 64) received 2 mA anodal 4 × 1 HD-tDCS or sham stimulation over the right DLPFC in two sessions and subsequently completed an N-back working memory task and a maximization scale (MS). We observed that maximization tendency scores increased during anodal stimulation. Furthermore, the results indicate that this increase in maximization tendency was driven by motivational changes. On the MS, alternative search subscale scores were significantly increased, demonstrating an increase in motivation to evaluate more alternatives; however, the results did not indicate that the increase in maximization tendencies was due to working memory improvement. These results demonstrated that maximization tendencies can be strengthened through noninvasive interventions and that the right DLPFC has a causal relationship with maximization tendencies.

Is evidential pluralism possible when we move to the social sciences, and if so, to what degree? What are the analytical benefits? The answer put forward in this article is that there is a tradeoff between how serious social science methodologies take the study of mechanisms and the analytical benefits that flow from evidential pluralism. In the social sciences, there are a range of different approaches to studying mechanisms, differentiated by (1) the degree to which the ‘process’ is unpacked theoretically, and (2) whether the approach takes seriously the particular nature of social phenomena and the epistemological consequences that flow from this, as in realist approaches to the study of mechanisms, or whether more neopositivist-based foundational assumptions are adopted. Depending on which approach to study mechanisms is used, evidential pluralism is either: easy but superficial, very productive but challenging, or almost impossible because of the fundamental differences between the types of claims being made and the forms of evidence used.

Background: The profession would benefit from greater and routine generation of causal evidence pertaining to the impact of music therapy interventions on client outcomes. Objective: One way to meet this goal is to revisit the use of Single Case Designs (SCDs) in clinical practice and research endeavors in music therapy. Given the appropriate setting and goals, this design can be accomplished with small sample sizes and it is often appropriate for studying music therapy interventions. Methods: In this article, we promote and discuss implementation of SCD studies in music therapy settings, review the meaning of internal study validity and by extension the notion of causality, and describe two of the most commonly used SCDs to demonstrate how they can help generate causal evidence to inform the field. Results: In closing, we describe the need for replication and future meta-analysis of SCD studies completed in music therapy settings. Conclusions: SCD studies are both feasible and appropriate for use in music therapy clinical practice settings, particularly for testing effectiveness of interventions for individuals or small groups.

Background Smokers tend to consume more coffee than non-smokers and there is evidence for a positive relationship between cigarette and coffee consumption in smokers. Cigarette smoke increases the metabolism of caffeine, so this association may represent a causal effect of smoking on caffeine intake. Methods We performed a Mendelian randomisation analysis in 114,029 individuals from the UK Biobank, 56,664 from the Norwegian HUNT study and 78,650 from the Copenhagen General Population Study. We used a genetic variant in the CHRNA5 nicotinic receptor (rs16969968) as a proxy for smoking heaviness. Coffee and tea consumption were self-reported. Analyses were conducted using linear regression and meta-analysed across studies. Results Each additional cigarette per day consumed by current smokers was associated with higher coffee consumption (0.10 cups per day, 95% CI:0.03,0.17). There was weak evidence for an increase in tea consumption per additional cigarette smoked per day (0.04 cups per day, 95% CI:-0.002,0.07). There was strong evidence that each additional copy of the minor allele of rs16969968 (which increases daily cigarette consumption) in current smokers was associated with higher coffee consumption (0.15 cups per day, 95% CI:0.11,0.20), but only weak evidence for an association with tea consumption (0.04 cups per day, 95% CI:-0.01,0.09). There was no clear evidence that rs16969968 was associated with coffee or tea consumption in never or former smokers. Conclusion These findings suggest that higher cigarette consumption causally increases coffee intake. This is consistent with faster metabolism of caffeine by smokers, but may also reflect behavioural links between smoking and coffee.

This article addresses the gap between what works in research and what works in practice. Currently, research in evidence-based education policy and practice focuses on randomized controlled trials. These can support causal ascriptions (“It worked”) but provide little basis for local effectiveness predictions (“It will work here”), which are what matter for practice. We argue that moving from ascription to prediction by way of causal generalization (“It works”) is unrealistic and urge focusing research efforts directly on how to build local effectiveness predictions. We outline various kinds of information that can improve predictions and encourage using methods better equipped for acquiring that information. We compare our proposal with others advocating a better mix of methods, like implementation science, improvement science, and practice-based evidence.

We make only one point in this article. Every quantitative study must be able to answer the question: what is your estimand? The estimand is the target quantity—the purpose of the statistical analysis. Much attention is already placed on how to do estimation; a similar degree of care should be given to defining the thing we are estimating. We advocate that authors state the central quantity of each analysis—the theoretical estimand—in precise terms that exist outside of any statistical model. In our framework, researchers do three things: (1) set a theoretical estimand, clearly connecting this quantity to theory; (2) link to an empirical estimand, which is informative about the theoretical estimand under some identification assumptions; and (3) learn from data. Adding precise estimands to research practice expands the space of theoretical questions, clarifies how evidence can speak to those questions, and unlocks new tools for estimation. By grounding all three steps in a precise statement of the target quantity, our framework connects statistical evidence to theory.

This chapter contains sections titled: Introduction A brief commentary on developments since 1970 Ambiguities of observational extensions Causal diagrams and structural equations Compelling versus plausible assumptions, models and inferences Nonidentification and the curse of dimensionality Identification in practice Identification and bounded rationality Conclusion Acknowledgments References

This chapter contains sections titled: Introduction Background Hypothesis The study Data and measures Methods Analysis Discussion Conclusion Acknowledgements Notes References

Background Understanding the mechanisms of implementation strategies (i.e., the processes by which strategies produce desired effects) is important for research to understand why a strategy did or did not achieve its intended effect, and it is important for practice to ensure strategies are designed and selected to directly target determinants or barriers. This study is a systematic review to characterize how mechanisms are conceptualized and measured, how they are studied and evaluated, and how much evidence exists for specific mechanisms. Methods We systematically searched PubMed and CINAHL Plus for implementation studies published between January 1990 and August 2018 that included the terms “mechanism,” “mediator,” or “moderator.” Two authors independently reviewed title and abstracts and then full texts for fit with our inclusion criteria of empirical studies of implementation in health care contexts. Authors extracted data regarding general study information, methods, results, and study design and mechanisms-specific information. Authors used the Mixed Methods Appraisal Tool to assess study quality. Results Search strategies produced 2277 articles, of which 183 were included for full text review. From these we included for data extraction 39 articles plus an additional seven articles were hand-entered from only other review of implementation mechanisms (total = 46 included articles). Most included studies employed quantitative methods (73.9%), while 10.9% were qualitative and 15.2% were mixed methods. Nine unique versions of models testing mechanisms emerged. Fifty-three percent of the studies met half or fewer of the quality indicators. The majority of studies (84.8%) only met three or fewer of the seven criteria stipulated for establishing mechanisms. Conclusions Researchers have undertaken a multitude of approaches to pursue mechanistic implementation research, but our review revealed substantive conceptual, methodological, and measurement issues that must be addressed in order to advance this critical research agenda. To move the field forward, there is need for greater precision to achieve conceptual clarity, attempts to generate testable hypotheses about how and why variables are related, and use of concrete behavioral indicators of proximal outcomes in the case of quantitative research and more directed inquiry in the case of qualitative research.