There has been enormous progress in psychotherapy research. This has culminated in recognition of several treatments that have strong evidence in their behalf. Even so, after decades of psychotherapy research, we cannot provide an evidence-based explanation for how or why even our most well studied interventions produce change, that is, the mechanism(s) through which treatments operate. This chapter presents central requirements for demonstrating mediators and mechanisms of change and reviews current data-analytic and designs approaches and why they fall short of meeting these requirements. The role of the therapeutic alliance in psychotherapy and cognitive changes in cognitive therapy for depression are highlighted to illustrate key issues. Promising lines of work to identify mediators and mechanisms, ways of bringing to bear multiple types of evidence, recommendations to make progress in understanding how therapy works, and conceptual and research challenges in evaluating mediators and mechanisms are also presented.

Discrepancies often exist among different informants' (e.g., parents, children, teachers) ratings of child psychopathology. Informant discrepancies have an impact on the assessment, classification, and treatment of childhood psychopathology. Empirical work has identified informant characteristics that may influence informant discrepancies. Limitations of previous work include inconsistent measurement of informant discrepancies and, perhaps most importantly, the absence of a theoretical framework to guide research. In this article, the authors present a theoretical framework (the Attribution Bias Context Model) to guide research and theory examining informant discrepancies in the clinic setting. Needed directions for future research and theory include theoretically driven attention to conceptualizing informant discrepancies across informant pairs (e.g., parent-teacher, mother-father, parent-child, teacher-child) as well as developing experimental approaches to decrease informant discrepancies in the clinic setting.Keywords: agreement, attribution bias context, correspondence, discrepancies, distortionIn a meta-analysis of 119 studies, Achenbach, McConaughy, and Howell (1987) identified what has come to be one of the most robust findings in clinical child research: Different informants' (e.g., parents, children, teachers) ratings of social, emotional, or behavior problems in children are discrepant (e.g., rs often in .20s). This finding has been replicated by further studies that have examined differences and similarities among informants' ratings under varying monikers (e.g., level of agreement among informants' ratings, disagreement among informants' ratings, correspondence among informants' ratings, discordance among informants' ratings). 1 Informant discrepancies have been found in virtually every method of clinical assessment that researchers and practitioners use to assess abnormal behavior in youths (e.g., rating scales, structured interviews; Achenbach et al., 1987;Grills & Ollendick, 2002). Moreover, discrepancies have been found in samples of informants encompassing diverse ethnic and cultural backgrounds (Hay et al., 1999;Jensen et al., 1999;Kaufman, Swan, & Wood, 1980;Krenke & Kollmar, 1998;Rohde et al., 1999;Rousseau & Drapeau, 1998;Verhulst, Althaus, & Berden, 1987) and in virtually any clinic sample in which discrepancies have been examined (Edelbrock, Costello, Dulcan, Conover, & Kala, 1986;Frank, Van Egeren, Fortier, & Chase, 2000;Frick, Silverthorn, & Evans, 1994;Hart, Lahey, Loeber, & Hanson, 1994;Kazdin, French, & Unis, 1983;Rapee, Barrett, Dadds, & Evans, 1994).The importance in studying informant discrepancies is highlighted by three key factors. First, there is no single measure or method of assessing psychopathology in children that provides a definitive or "gold standard" to gauge which children are experiencing a given set of problems or disorders (e.g., Richters, 1992). The lack of such a standard stems, in part, from the need to incorporate information from multiple informants to asses...

Terms such as risk, risk factors, and especially the term cause are inconsistently and imprecisely used, fostering scientific miscommunication and misleading research and policy. Clarifying such terms is the essential first step. We define risk and a risk factor (protective factor) and their potency, set out the conceptual basis of the methods by which risk factors are identified and potency demonstrated, and propose criteria for establishing the status of a risk factor as a fixed or variable marker or a causal risk factor. All definitions are based on the state of scientific knowledge (empirical documentation), rather than on hypotheses, speculations, or beliefs. We discuss common approaches and pitfalls and give a psychiatric research example. Imprecise reports can impede the search for understanding the cause and course of any disease and also may be a basis of inadequate clinical or policy decision-making. The issues in risk research are much too important to tolerate less than precise terminology or the less than rigorous research reporting that results from imprecise and inconsistent terminology.

Psychological interventions to treat mental health issues have developed remarkably in the past few decades. Yet this progress often neglects a central goal-namely, to reduce the burden of mental illness and related conditions. The need for psychological services is enormous, and only a small proportion of individuals in need actually receive treatment. Individual psychotherapy, the dominant model of treatment delivery, is not likely to be able to meet this need. Despite advances, mental health professionals are not likely to reduce the prevalence, incidence, and burden of mental illness without a major shift in intervention research and clinical practice. A portfolio of models of delivery will be needed. We illustrate various models of delivery to convey opportunities provided by technology, special settings and nontraditional service providers, self-help interventions, and the media.

Classifying putative risk factors into these qualitatively different types can help identify high-risk individuals in need of preventive interventions and can help inform the content of such interventions. These methods may also help bridge the gaps between theory, the basic and clinical sciences, and clinical and policy applications and thus aid the search for early diagnoses and for highly effective preventive and treatment interventions.