Protective Mechanisms for Depression among Racial/Ethnic Minority Youth: Empirical Findings, Issues, and Recommendations

We (1) review empirical studies that report findings regarding putative protective mechanisms when exposed to risk of depression in African American and Hispanic adolescents; (2) identify key protective mechanisms for different risk contexts that garner empirical support; (3) synthesize the mechanisms identified as protective against depression among racial/ethnic minority adolescents; and (4) discuss improved methods for advancing understanding of resilience against depression in minority youth. The studies were selected from PsycINFO searches that met the following inclusion criteria: participants between 12 and 21 years of age, inclusions of racial/ethnic minority members, examining protection through an interaction with a risk factor, and outcome measures of depression, depressed mood, or depressive symptomatology. We found 39 eligible studies; 13 of which included multiple racial/ethnic groups. The following were supported as protective mechanisms, at least preliminarily, for at least one racial/ethnic group and in at least one risk context: employment, extracurricular activities, father–adolescent closeness, familism, maternal support, attending predominately minority schools, neighborhood composition, non-parent support, parental inductive reasoning, religiosity, self-esteem, social activities, and positive early teacher relationships. To investigate protective mechanisms more comprehensively and accurately across individual, social, and community levels of influence, we recommend incorporating multilevel modeling or multilevel growth curve analyses and large diverse samples

Webometrics benefitting from web mining? An investigation of methods and applications of two research fields

Webometrics and web mining are two fields where research is focused on quantitative analyses of the web. This literature review outlines definitions of the fields, and then focuses on their methods and applications. It also discusses the potential of closer contact and collaboration between them. A key difference between the fields is that webometrics has focused on exploratory studies, whereas web mining has been dominated by studies focusing on development of methods and algorithms. Differences in type of data can also be seen, with webometrics more focused on analyses of the structure of the web and web mining more focused on web content and usage, even though both fields have been embracing the possibilities of user generated content. It is concluded that research problems where big data is needed can benefit from collaboration between webometricians, with their tradition of exploratory studies, and web miners, with their tradition of developing methods and algorithms