Depression, suicide, and suicide prevention in schizophrenia

Suicide is the single largest cause of premature death among individuals with schizophrenia. Furthermore, epidemiological data indicate that nearly 80% of patients with the diagnosis of schizophrenia will experience a major depressive episode at some time during their lifetime. This report reviews recent findings relative to the risk of suicide in schizophrenia, including data from the Chestnut Lodge longitudinal study of schizophrenia subtypes and symptom domains. Paradoxically, those patients with schizophrenia who are most likely to recover or experience a good outcome are also those at greatest risk for suicide. The reduction of morbidity and mortality in schizophrenia should include depression and suicidality as targets for both psychopharmacological and psychosocial treatment.     

Ceteris Paribus Laws and Minutis Rectis Laws

Special science generalizations admit of exceptions. Among the class of non‐exceptionless special science generalizations, I distinguish (what I will call) minutis rectis (mr) generalizations from the more familiar category of ceteris paribus (cp) generalizations. I argue that the challenges involved in showing that mr generalizations can play the law role are underappreciated, and quite different from those involved in showing that cp generalizations can do so. I outline a strategy for meeting the challenges posed by mr generalizations.     

Recruitment Strategies for a Home-Based Physical Activity Intervention for Breast Cancer Patients

Recruiting cancer patients for randomized trials investigating psychosocial interventions presents several unique challenges. This paper describes the challenges and yields for different recruitment methods used in Moving Forward, a randomized trial of a home-based moderate-intensity physical activity program for early-stage breast cancer survivors. Recruitment methods included letters sent to patients by their oncologists, direct referrals from oncologists, in-person recruitment in oncology clinics, referrals from nurses and medical assistants, passive recruitment, other mailings, and community outreach strategies. Of the 424 screenings completed, 86 (20.3%) participants were randomized. Physician letters yielded the greatest number of initial screenings (147; 34.7%) and participants randomized (28; 32.5%). In-person recruitment also proved to be a productive recruitment strategy; 14 (16.3%) of the participants were recruited via this strategy. Community outreach efforts did not provide as great a yield and were labor intensive. We discuss suggestions for recruitment of cancer patients in future trials.     

A General Structure for Legal Arguments About Evidence Using Bayesian Networks

A Bayesian network (BN) is a graphical model of uncertainty that is especially well suited to legal arguments. It enables us to visualize and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs have been widely discussed and recently used in the context of legal arguments, there is no systematic, repeatable method for modeling legal arguments as BNs. Hence, where BNs have been used in the legal context, they are presented as completed pieces of work, with no insights into the reasoning and working that must have gone into their construction. This means the process of building BNs for legal arguments is ad hoc, with little possibility for learning and process improvement. This article directly addresses this problem by describing a method for building useful legal arguments in a consistent and repeatable way. The method complements and extends recent work by Hepler, Dawid, and Leucari (2007) on object‐oriented BNs for complex legal arguments and is based on the recognition that such arguments can be built up from a small number of basic causal structures (referred to as idioms). We present a number of examples that demonstrate the practicality and usefulness of the method.