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: Anxiety, although as common and arguably as debilitating as depression, has garnered less attention, and is often undetected and undertreated in the general population. Similarly, anxiety among medical students warrants greater attention due to its significant implications. We aimed to study the global prevalence of anxiety among medical students and the associated factors predisposing medical students to anxiety. In February 2019, we carried out a systematic search for cross-sectional studies that examined the prevalence of anxiety among medical students. We computed the aggregate prevalence and pooled odds ratio (OR) using the random-effects model and used meta-regression analyses to explore the sources of heterogeneity. We pooled and analyzed data from sixty-nine studies comprising 40,348 medical students. The global prevalence rate of anxiety among medical students was 33.8% (95% Confidence Interval: 29.2–38.7%). Anxiety was most prevalent among medical students from the Middle East and Asia. Subgroup analyses by gender and year of study found no statistically significant differences in the prevalence of anxiety. About one in three medical students globally have anxiety—a prevalence rate which is substantially higher than the general population. Administrators and leaders of medical schools should take the lead in destigmatizing mental illnesses and promoting help-seeking behaviors when students are stressed and anxious. Further research is needed to identify risk factors of anxiety unique to medical students.

Objective: This meta-analysis aimed to estimate the global lifetime and 12-month prevalence of suicidal behavior, deliberate self-harm and non-suicidal self-injury in children and adolescents. Methods: A systematic search for relevant articles published between 1989 to 2018 was performed in multiple electronic databases. The aggregate 12-month and lifetime prevalence of suicidal behavior, deliberate self-harm, and non-suicidal self-injury were calculated based on the random-effects model. Subgroup analyses were performed to compare the prevalence according to school attendance and geographical regions. Results: A total of 686,672 children and adolescents were included. The aggregate lifetime and 12-month prevalence of suicide attempts was 6% (95% CI: 4.7–7.7%) and 4.5% (95% CI: 3.4–5.9%) respectively. The aggregate lifetime and 12-month prevalence of suicidal plan was 9.9% (95% CI: 5.5–17%) and 7.5% (95% CI: 4.5–12.1%) respectively. The aggregate lifetime and 12-month prevalence of suicidal ideation was 18% (95% CI: 14.2–22.7%) and 14.2% (95% CI: 11.6–17.3%) respectively. The aggregate lifetime and 12-month prevalence of non-suicidal self-injury was 22.1% (95% CI: 16.9–28.4%) and 19.5% (95% CI: 13.3–27.6%) respectively. The aggregate lifetime and 12-month prevalence of deliberate self-harm was 13.7% (95% CI: 11.0–17.0%) and 14.2% (95% CI: 10.1–19.5%) respectively. Subgroup analyses showed that full-time school attendance, non-Western countries, low and middle-income countries, and geographical locations might contribute to the higher aggregate prevalence of suicidal behaviors, deliberate self-harm, and non-suicidal self-injury. Conclusions: This meta-analysis found that non-suicidal self-injury, suicidal ideation, and deliberate self-harm were the three most common suicidal and self-harm behaviors in children and adolescents.

This study examined the neuropsychiatric sequelae of acutely ill patients with coronavirus disease 2019 (COVID-19) infection who received treatment in hospital isolation wards during the COVID-19 pandemic. Ten COVID-19 patients who received treatment in various hospitals in Chongqing, China; 10 age- and gender-matched psychiatric patients; and 10 healthy control participants residing in the same city were recruited. All participants completed a survey that collected information on demographic data, physical symptoms in the past 14 days and psychological parameters. Face-to-face interviews with COVID-19 patients were also performed using semi-structured questions. Among the COVID-19 patients, 40% had abnormal findings on the chest computed topography scan, 20% had dysosmia, 10% had dysgeusia, and 80% had repeated positivity on COVID-19 reverse-transcription polymerase chain reaction testing. COVID-19 and psychiatric patients were significantly more worried about their health than healthy controls (p = 0.019). A greater proportion of COVID-19 patients experienced impulsivity (p = 0.016) and insomnia (p = 0.039) than psychiatric patients and healthy controls. COVID-19 patients reported a higher psychological impact of the outbreak than psychiatric patients and healthy controls, with half of them having clinically significant symptoms of posttraumatic stress disorder. COVID-19 and psychiatric patients had higher levels of depression, anxiety and stress than healthy controls. Three themes emerged from the interviews with COVID-19 patients: (i) The emotions experienced by patients after COVID-19 infection (i.e., shock, fear, despair, hope, and boredom); (ii) the external factors that affected patients’ mood (i.e., discrimination, medical expenses, care by healthcare workers); and (iii) coping and self-help behavior (i.e., distraction, problem-solving and online support). The future direction in COVID-19 management involves the development of a holistic inpatient service to promote immune and psychological resilience.

Background: In psychiatry, stigma is an attitude of disapproval towards people with mental illnesses. Psychiatric disorders are common in Asia but some Asians receive inadequate treatment. Previous review found that Asians with mental illness were perceived to be dangerous and aggressive. There is a need for renewed efforts to understand stigma and strategies which can effectively reduce stigma in specific Asian societies. The objective of this systematic review was to provide an up-to-date overview of existing research and status on stigma experienced by psychiatric patients and anti-stigma campaigns in China, Hong Kong, Japan, Singapore, Korea, and Thailand. Methods: A systematic literature search was conducted in the following databases, including PubMed, PsycINFO, Embase, Web of Science, and local databases. Studies published in English and the official language of included countries/territories were considered for inclusion in the systematic review. Any article on stigma related to any form of psychiatric illness in the six Asian societies was included. Results: One hundred and twenty-three articles were included for this systematic review. This review has six major findings. Firstly, Asians with mental illnesses were considered as dangerous and aggressive, especially patients suffering from schizophrenia and bipolar disorder; second, psychiatric illnesses in Asian societies were less socially-acceptable and were viewed as being personal weaknesses; third, stigma experienced by family members was pervasive and this is known as family stigma; fourth, this systemic review reported more initiatives to handle stigma in Asian societies than a decade ago; fifth, there have been initiatives to treat psychiatric patients in the community; and sixth, the role of supernatural and religious approaches to psychiatric illness was not prevailing. Conclusion: This systematic review provides an overview of the available scientific evidence that points to areas of needed intervention to reduce and ultimately eliminate inequities in mental health in Asia.

Neural models with minimal feature engineering have achieved competitive performance against traditional methods for the task of Chinese word segmentation. However, both training and working procedures of the current neural models are computationally inefficient. This paper presents a greedy neural word segmenter with balanced word and character embedding inputs to alleviate the existing drawbacks. Our segmenter is truly end-toend, capable of performing segmentation much faster and even more accurate than state-of-the-art neural models on Chinese benchmark datasets.

Cross-lingual transfer, where a high-resource transfer language is used to improve the accuracy of a low-resource task language, is now an invaluable tool for improving performance of natural language processing (NLP) on lowresource languages. However, given a particular task language, it is not clear which language to transfer from, and the standard strategy is to select languages based on ad hoc criteria, usually the intuition of the experimenter. Since a large number of features contribute to the success of cross-lingual transfer (including phylogenetic similarity, typological properties, lexical overlap, or size of available data), even the most enlightened experimenter rarely considers all these factors for the particular task at hand. In this paper, we consider this task of automatically selecting optimal transfer languages as a ranking problem, and build models that consider the aforementioned features to perform this prediction. In experiments on representative NLP tasks, we demonstrate that our model predicts good transfer languages much better than ad hoc baselines considering single features in isolation, and glean insights on what features are most informative for each different NLP tasks, which may inform future ad hoc selection even without use of our method. 1 * Equal contribution 1 Code, data, and pre-trained models are available at