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Terrorism is a form of psychological warfare. A successful terrorist attack results in mass violence and can produce a man-made disaster. After any disaster, mental health professionals can play important but varied roles. In the immediate aftermath, the mental health professional's role is shaped by a need for flexibility and pragmatism. As time passes and things begin to return to normal, the role moves toward more typical mental health services—a treatment role, shaped by the preceding trauma. Mental health professionals can also provide treatment to accused or convicted terrorists in correctional settings. Alternatively, mental health professionals can serve in an evaluator role. The evaluator role is forensic and involves the evaluation of an accused or convicted terrorist (eg, for competency to stand trial, criminal responsibility, or sentence mitigation) or victim (eg, for psychological damages). [ Psychiatr Ann . 2020;50(9):382–386.]

This article explores the extant literature on outcomes of mass trauma in children as well as factors that predict risk and interventions used to enhance resilience and promote recovery. Many children display acute stress reactions after a mass trauma and some will develop chronic symptoms, but most will recover or demonstrate resilience. Children's reactions include posttraumatic stress symptoms, depression, anxiety, and behavior problems. Various factors predict resilience and vulnerability in children after a mass trauma. These include preexisting factors such as the child's demographics, personality, and previous trauma exposure. The type and degree of exposure to the trauma influences outcomes, with direct exposure or loss of loved ones contributing to worse outcomes. The postdisaster environment, including disaster-related disruption and family and social factors, may hinder or enhance a child's recovery. The aforementioned factors may be used to inform assessment of risk and resilience in public and mental health settings in children after a mass trauma. Effective interventions vary based on the child's exposure and symptomology, timing of intervention in relation to the trauma, and the resources available for service provision. [ Psychiatr Ann . 2020;50(9):387–392.]

Mental health providers encounter and provide treatment to patients who identify as lesbian, gay, bisexual, transgender, and queer/questioning (LGBTQ+) in various treatment settings. Understanding this unique population is essential in delivering culturally competent, appropriate, and effective care to LGBTQ+ patients. This article begins by discussing the existing stigma and the resulting disparities in mental health problems for LGBTQ+ people compared to their cisgender heterosexual counterparts, such as the increased risk of suicide. Then, presented are practical adaptations for the psychological assessment and treatment of LGBTQ+ patients that maintain fidelity to treatment protocols while allowing for flexibility in addressing their unique values, preferences, and challenges. [ Psychiatr Ann. 2019;49(10):436–440.]

Lesbian, gay, bisexual, transgender, and queer/questioning (LGBTQ+) people often struggle to find quality medical and mental health care. It is important that mental health providers have a general knowledge of the specific needs of the LGBTQ+ population. This article describes how to establish a safe environment for LGBTQ+ patients, as well as how to address their specific mental health and medical needs. Knowledge of these needs can decrease overall morbidity and mortality, thus improving outcomes for this population. [ Psychiatr Ann. 2019;49(10):426–435.]

Lesbian, gay, bisexual, transgender, and queer/questioning (LGBTQ+) people are sexual minorities and have higher rates of substance use disorders, mental illness, and trauma, which translates into compounded stigma for them. Compounded stigma is the additive and cumulative impact of belonging to one, or several, marginalized groups (eg, racial/ethnic minority, LGBTQ+) and also suffering from addiction, mental illness, and/or trauma. There are important interactions among these comorbidities, trauma, and sexual minority status that affect patient behavior, treatment planning, and treatment outcomes. This article presents a Venn diagram as a helpful clinical tool to visually illustrate the complex interactions of mental illness, addictions, trauma, and LGBTQ+ minority status for both health care providers and patients. Having an awareness and understanding of these important interactions can enhance patient rapport and treatment experience as well as treatment planning. [ Psychiatr Ann. 2019;49(10):446–452.]

To assess effects of low-level continuous positive airway pressure (CPAP) on snoring in habitual snorers without obstructive sleep apnea (OSA).A multicenter prospective in-laboratory reversal crossover intervention trial was conducted between September 2013 and August 2014. Habitual snorers were included if they snored (inspiratory sound pressure level ≥ 40 dBA) for ≥ 30% all sleep breaths on a baseline sleep study (Night 1), and if significant OSA and daytime somnolence were absent. Included participants then underwent a CPAP titration study at 2, 4, or 6 cm HOn baseline sleep studies, participants demonstrated snoring at ≥ 40 dBA on 53 ± 3% and ≥ 45 dBA on 35 ± 4% of breaths. Snoring frequency decreased progressively as nasal pressure increased from 0 to 4 cm HLow-level CPAP below the range required to treat OSA diminished nocturnal snoring, and produced uniform reduction in nightly noise production below the World Health Organization's limit of 45 dBA.ClinicalTrials.gov, identifier: NCT01949584.

Somatic symptom and other related disorders are chronic and difficult psychiatric presentations for providers to assess, diagnose, and treat and are seen more in the medical than psychiatric setting. Because patients with these disorders are commonly referred to the consultation-liaison service, it is important for hospital-based mental health professionals to understand these illnesses. Identifying and differentiating between these diagnoses requires an understanding of several factors, including the patient's medical history, circumstances that may be driving behavior, and careful evaluation of the patient's awareness of these factors. Such factors are also essential in directing treatment, which tends to be time intensive and prolonged. This article provides an illustrative case and general guidelines for the diagnosis and treatment of illnesses under the category of somatic symptoms and related disorders. [ Psychiatr Ann . 2017;47(4):184–191.]

Introduction We developed and validated an abbreviated Digital Sleep Questionnaire (DSQ) to identify common societal sleep disturbances including insomnia, delayed sleep phase syndrome (DSPS), insufficient sleep syndrome (ISS), and risk for obstructive sleep apnea (OSA). Methods The DSQ was administered to 3799 community volunteers, of which 2113 were eligible and consented to the study. Of those, 247 were interviewed by expert sleep physicians, who diagnosed ≤2 sleep disorders. Machine Learning (ML) trained and validated separate models for each diagnosis. Regularized linear models generated 15–200 features to optimize diagnostic prediction. Models were trained with five-fold cross-validation (repeated five times), followed by robust validation testing. ElasticNet models were used to classify true positives and negatives; bootstrapping optimized probability thresholds to generate sensitivities, specificities, accuracies, and area under the receiver operating curve (AUC). Results Compared to reference subgroups, physician-diagnosed sleep disorders were marked by DSQ evidence of sleeplessness (insomnia, DSPS, OSA), sleep debt (DSPS, ISS), airway obstruction during sleep (OSA), blunted circadian variability in alertness (DSPS), sleepiness (DSPS and ISS), increased alertness (insomnia) and global impairment in sleep-related quality of life (all sleep disorders). ElasticNet models validated each diagnosis with high sensitivity (80–83%), acceptable specificity (63–69%), high AUC (0.80–0.85) and good accuracy (agreement with physician diagnoses, 68–73%). Discussion A brief DSQ readily engaged and efficiently screened a large population for common sleep disorders. Powered by ML, the DSQ can accurately classify sleep disturbances, demonstrating the potential for improving the sleep, health, productivity and safety of populations.