Systematic reviews are characterized by a methodical and replicable methodology and presentation. They involve a comprehensive search to locate all relevant published and unpublished work on a subject; a systematic integration of search results; and a critique of the extent, nature, and quality of evidence in relation to a particular research question. The best reviews synthesize studies to draw broad theoretical conclusions about what a literature means, linking theory to evidence and evidence to theory. This guide describes how to plan, conduct, organize, and present a systematic review of quantitative (meta-analysis) or qualitative (narrative review, meta-synthesis) information. We outline core standards and principles and describe commonly encountered problems. Although this guide targets psychological scientists, its high level of abstraction makes it potentially relevant to any subject area or discipline. We argue that systematic reviews are a key methodology for clarifying whether and how research findings replicate and for explaining possible inconsistencies, and we call for researchers to conduct systematic reviews to help elucidate whether there is a replication crisis.

There is a burgeoning literature examining perceptions of being defeated or trapped in different psychiatric disorders. The disorders most frequently examined to date are depression, anxiety problems, posttraumatic stress disorder (PTSD), and suicidality. To quantify the size and consistency of perceptions of defeat and entrapment in depression, anxiety problems, PTSD and suicidality, test for differences across psychiatric disorders, and examine potential moderators and publication bias. Random-effects meta-analyses based on Pearson's correlation coefficient r. Forty studies were included in the meta-analysis (n = 10,072). Perceptions of defeat and entrapment were strong (around r = 0.60) and similar in size across all four psychiatric disorders. Perceptions of defeat were particularly strong in depression (r = 0.73). There was no between-study heterogeneity; therefore moderator analyses were conducted in an exploratory fashion. There was no evidence of publication bias. Analyses were cross-sectional, which precludes establishing temporal precedence or causality. Some of the meta-analyses were based on relatively small numbers of effect sizes, which may limit their generalisability. Perceptions of defeat and entrapment are clinically important in depression, anxiety problems, PTSD, and suicidality. Similar-sized, strong relationships across four different psychiatric disorders could suggest that perceptions of defeat and entrapment are transdiagnostic constructs. The results suggest that clinicians and researchers need to become more aware of perceptions of defeat and entrapment.

Two core but untested predictions of Positive Clinical Psychology (PCP) are that (1) Many psychiatric problems can be understood as one end of bipolar continua with well-being, and (2) that reducing psychiatric symptoms will provide an equal (near linear) decrease in risk for several other psychiatric variables, irrespective of position on continua. We test these predictions in relation to a purported well-being/depression continuum, as measured by the Center for Epidemiologic Studies-Depression (CES-D), a popular measure of depressive experiences in research and clinical practice. A large (N=4138), diverse sample completed the CES-D, which contains a mixture of negatively worded and positively worded items (e.g., "I felt sad," "I enjoyed life"). The latter are conventionally reverse scored to compute a total score. We first examined whether purportedly separate well-being and depression CES-D factors can be reconceptualised as a bipolar well-being/depression continuum. We then characterised the (linear or nonlinear) form of the relationship between this continuum and other psychiatric variables. Both predictions were supported. When controlling for shared method bias amongst positively worded items, a single factor well-being/depression continuum underlies the CES-D. Baseline levels on this continuum are found to have near linear relationships with changes in anxiety symptoms, aggression, and substance misuse over time, demonstrating that moving from depression to well-being on the CES-D provides an equal decrease in risk for several other psychological problems irrespective of position on the continuum. The CES-D does not measure well-being as comprehensively as established scales of well-being. Results support calls for mental health services to jointly focus on increasing well-being and reducing distress, and point to the value of early intervention and instilling resilience in order to prevent people moving away from high levels of well-being.

Machine learning (ML) is an increasingly popular approach/technique for analyzing "Big Data" and predicting risk behaviors and psychological problems. However, few published critiques of ML as an approach currently exist. We discuss some fundamental cautions and concerns with ML that are relevant when attempting to predict all clinical and forensic risk behaviors (risk to self, risk to others, risk from others) and mental health problems. We hope to provoke a healthy scientific debate to ensure that ML's potential is realized and to highlight issues and directions for future risk prediction, assessment, management, and prevention research. ML, by definition, does not require the model to be specified by the researcher. This is both its key strength and its key weakness. We argue that it is critical that the ML algorithm (the model or models) and the results are both presented and that ML needs to be become machine-assisted learning like other statistical techniques; otherwise, we run the risk of becoming slaves to our machines. Emerging evidence potentially challenges the superiority of ML over other approaches, and we argue that ML's complexity significantly limits its clinical utility. Based on the available evidence, we believe that researchers and clinicians should emphasize identifying, understanding, and explaining (formulating) individual clinical needs and risks and providing individualized management and treatment plans, rather than trying to predict or putting too much trust in predictions that will inevitably be wrong some of the time (and we do not know when). (PsycINFO Database Record (c) 2020 APA, all rights reserved).

SUMMARY: Post-traumatic stress disorder (PTSD) is a complex, heterogeneous mental health problem that can be challenging to identify, assess, understand, diagnose and treat. This article provides an overview and critique of key topics, literature and principles to inform comprehensive and meticulous assessment of PTSDs. Although expert witnesses are the target audience, this article will have relevance for identifying, assessing, understanding and diagnosing PTSDs in all clinical contexts. A range of topics relevant to assessment are discussed, including: the complex relationship between trauma and PTSDs; DSM-5-TR PTSD and ICD-11 PTSD and complex PTSD diagnoses and the similarities and differences between them; the clinical presentation of PTSDs; psychological models of PTSDs; how to approach assessment and differential diagnosis; the impact of PTSD on neuropsychological abilities and functioning (disability); causation, reliability and assessing PTSDs when this is being considered as a legal defence; evidence-based interventions (medication, psychological therapy, when is the ‘right time’ for therapy, contraindications); and prognosis (if untreated, how long therapy/change takes). Given ongoing debate, the article proposes that trauma exposure is best defined in future iterations of the DSM and ICD as exposure to one or more psychologically threatening or horrific experiences that are overwhelming.