Psychometric Properties of the Persian Version of the Teasing Questionnaire 23

The current study was a cross-sectional research and aimed to investigate the factor structure, internal consistency, and validities of the Persian version of the Teasing Questionnaire-Revised (TQ-R). Forward and backward translations of the TQ-R were performed; face and content validities were determined based on comments by a sample of psychology students and specialists. Using the cluster sampling method, 290 participants were recruited, and 201 valid data (Mage = 23.53, SD = 3.53, 64.2 men) were analyzed. The factor structure was assessed by confirmatory and exploratory factor analysis (EFA). The result of the confirmatory factor analysis(es) did not confirm the proposed three, four, and five-factor models. EFA revealed four factors with 23 items, explaining 52.03 of the total variance. The internal consistency of the Persian version of Teasing Questionnaire 23 was in the excellent range (α = 0.92), and its expected associations with external correlates (e.g., depression and anxiety) supported the measure�s convergent validity. The findings indicated that the Persian version of the TQ-R has sound psychometric properties and can be used as a valid and reliable tool in research and clinical outcomes. © Copyright © 2021 Ebrahimi, Elhami Athar, Hakim Shooshtari, Karsazi and Storch

Exposure therapy for specific phobias in children and adolescents

Among children and adolescents, specific phobia is one of the most common anxiety disorders. Although phobias are usually considered to have a relatively "simple" clinical presentation, they can interfere substantially with daily life for youth. Evidence-based treatments are available for specific phobia in youth. In all evidence-based treatments, exposure is the key working element. This chapter advises clinicians how to setup and carry out exposure treatment for specific phobia with children and adolescents in clinical practice. This chapter offers recommendations on the assessment process, developing and carrying out a treatment plan, involving the family in treatment, maintaining therapeutic improvements and handling challenging issues

Promoting tech transfer between space and global mental health

INTRODUCTION: Numerous issues in mental health benefit from technological innovation. An example involves the mental health challenges of long-duration spaceflight (such as a Mars mission), including prolonged confinement, microgravity, and different sunlight exposure lengths. Persisting on Earth are global mental health challenges stemming from disease burdens, limited interview-based diagnostic systems, trial-and-error treatment approaches, and suboptimal access. There is potential for cross-pollinating solutions between these seemingly disparate challenges using a range of emerging technologies such as sensors, omics, and big data. In this review, we highlight the bidirectional value of mental health technology transfer aimed to address issues both on Earth and in space. METHODS: We prepared a systematic review of studies pertaining to mental health technological innovation and space medicine. RESULTS: For Earth mental health technologies translatable to long-duration space missions, we cite several example technologies, including device-based psychotherapy and social support, conversational agents aka chatbots, and nutritional and physical activity focused mental health. Space technologies translatable to Earth mental health include remote sensing devices, global navigation satellite systems, satellite communications, chronotherapies, and nutritional advances. DISCUSSION: There is a rich history of space technologies informing Earth technological trends, including general health care on Earth, and vice versa. To avoid the traditional happenstance approach that results in delays, missed opportunities, and increased cost, and to improve outcomes for both Earth and space utilization of these technologies, we propose increased dialogue and training opportunities to enhance innovation and outcomes.Donald D. Chang, Eric A. Storch, Lance Black, Michael Berk, Neal Pellis ... Harris A. Eyre ... et al