Hohsu9254

The cross-sectional design limited casual inferences between variables. The effects of domain-specific autonomy support were not involved in this study, and other mediators between autonomy support and depressive symptoms and more sociodemographic variables should be considered.

Autonomy support from both parents and friends might be protective factors against depressive symptoms in Chinese gay men. Friends' autonomy support was related to decreased depressive symptoms via lower internalized homonegativity and rumination, while parental autonomy support was related to less depressive symptoms through other possible mechanisms.

Autonomy support from both parents and friends might be protective factors against depressive symptoms in Chinese gay men. Friends' autonomy support was related to decreased depressive symptoms via lower internalized homonegativity and rumination, while parental autonomy support was related to less depressive symptoms through other possible mechanisms.

It is necessary to develop effective preventive interventions before depression established to alleviate depressive symptoms or delay the onset of depression. In this study, we employed Bayesian network meta-analysis to identify the optimal psychosocial intervention approach for preventing depressive symptoms in children and adolescents.

We searched publication databases and conference abstracts, from time of their inception through April 2019 without language restriction, for randomized controlled trials that compared the efficacy of various psychosocial intervention approaches. We extracted the mean and standarddeviation values between baseline and the last observation, and calculated the change score in depression. We also assessed ranking probability by surface under the cumulative ranking curve using a 95% credible interval.

A total of 27 randomized controlled trials, involving 5,976 participants aged between 7 to 18 years, were included in our analyses. Analysis of various valid assessment instruments indicated that computer cognitive-behavioral therapy [standard mean difference (SMD=-1.82)], cognitive-behavioral therapy (SMD=-1.54) and interpersonal psychotherapy (SMD=-1.29) were statistically superior to wait-list group. Among the approaches, computer cognitive-behavioral therapy had the highest probability of being the best intervention, based on improvement from baseline to the end of the intervention (SUCRA=90.47%, CrI 0.55, 1.00).

The results herein may not apply to other cultures and ethnic minorities because about half of the studies included in our analysis were conducted in the United States.

Computer cognitive-behavioral therapy was the most recommended intervention to accompany the depression among children and adolescents according to our Bayesian network meta-analysis results.

Computer cognitive-behavioral therapy was the most recommended intervention to accompany the depression among children and adolescents according to our Bayesian network meta-analysis results.

Depression is a common and sometimes severe form of mental illness, and public attitudes towards depression can impact the psychological and social functioning of depressed patients. The purpose of the present study was to investigate public attitudes toward depression and three-year trends in these attitudes using big data analysis of social media posts in China.

A search of publically available Sina Weibo posts from January 2014 to July 2017 identified 20,129 hot posts with the keyword term "depression". We first used a Chinese Linguistic Psychological Text Analysis System (TextMind) to analyze linguistic features of the posts. And, then we used topic models to conduct semantic content analysis to identify specific themes in Weibo users' attitudes toward depression.

Linguistic features analysis showed a significant increase over time in the frequency of terms related to affect, positive emotion, anger, cognition (including the subcategory of insight), and conjunctions. Semantic content analysis identi in mentions of social support for depressed persons. A supervised learning statistical model can be developed in future research to provide an even more precise analysis of specific attitudes.

This study aimed at describing self-harm and suicidality (SHS) in relation to unobserved heterogeneous groups of college students based on their psychiatric symptoms. SHS of each latent class were examined by race/ethnicity to inform risk factors relevant to subgroups of U.S. college population.

The participants (N=42,779) were drawn from the Spring 2017 American College Health Association-National College Health Assessment II (ACHA-NCHA II) Reference Group. Latent class analysis (LCA) was conducted based on participants' reports of past-year psychiatric symptoms. The reported SHS were examined by the latent class of students and their race/ethnicity.

LCA identified two latent classes The Emotional Exhaustion (EE) class and the Multiple Psychiatric Symptoms (MPS) class. Within the EE class, Black students were at the greatest risk for exhibiting suicide intent and attempted suicide. Within the MPS class, Multiracial students showed the highest odds of self-harm and suicidal intent, and Black students showed the highest odds of attempted suicide, followed by Asians/Pacific Islanders.

The findings were based on a cross-sectional dataset that did not inform the temporal relations of psychiatric symptoms and SHS.

Utilizing a person-centered latent class analysis, this study revealed that Black students were of the greatest concern for SHS among those who reported only common symptoms of emotional exhaustion. The findings highlight the importance of developing preventive and remedial models that address unique risk factors and mental health needs for various subgroups of U.S. H-1152 inhibitor college population.

Utilizing a person-centered latent class analysis, this study revealed that Black students were of the greatest concern for SHS among those who reported only common symptoms of emotional exhaustion. The findings highlight the importance of developing preventive and remedial models that address unique risk factors and mental health needs for various subgroups of U.S. college population.