Article 1:

 

Abstract:

This article examines the association between mental health disorders and being identified as a bully among children between the ages of 6 and 17 years. Data from the 2007 National Survey of Children’s Health were examined. A total of 63,997 children had data for both parental reported mental health and bullying status. Bivariate analysis and logistic regression was performed to assess the association between mental health status and being identified as a bully with an age-stratified analysis and sub-analysis by type of mental health disorder. In 2007, 15.2% of U.S. children ages 6 to 17 years were identified as bullies by their parent or guardian. Children with a diagnosis of depression, anxiety, or depression had a threefold increased odds of being a bully. The diagnosis of depression is associated with a 3.31 increased odds (95% CI = [2.7, 4.07]) of being identified as a bully. Children with anxiety and attention deficit and hyperactivity disorder (ADHD) had similar odds. The diagnosis of a mental health disorder is strongly associated with being identified as a bully. In particular, depression, anxiety, and ADHD are strongly associated with being identified as a bully. These findings emphasize the importance of providing psychological support to not only victims of bullying but bullies as well. Understanding the risk profile of childhood bullies is essential in gaining a better grasp of this public health problem and in creating useful and appropriate resources and interventions to decrease bullying.

 

Questions to consider:

1. Why are children with mental health issues at a higher risk for victimizing others?

2. Describe the mental health disorders associated with bully behavior.

3. Explain why it is important to not only focus on the victims of bullying but also those labeled as a bully.

 

Article 2:

 

Abstract:

The Patient Protection and Affordable Care Act (ACA) will greatly increase the demand for mental health (MH) services, as 62.5 million Americans from relatively high-need populations will be newly eligible for MH benefits. Consequently, the supply of MH care provider services is expected to proportionately decrease by 18% to 21% in 2014. ACA funding does not demonstrate the ability to increase turnout of psychiatrists sufficiently to meet the need. Available data indicate that the numbers of advanced practice psychiatric nurses (APPNs) continue to increase at a much greater rate, but information from either a clinical perspective or a market perspective is complicated by the weak distinctions that are made between nurse practitioners (NPs) and other nonphysician care professionals. The following recommendations are made: (a) some of the ACA funding for research into efficient and effective care delivery systems should be allocated to acquiring data on APPNs in leadership roles or clinical settings in which they are ultimately responsible for management of MH care, as differentiated from settings in which they provide support for psychiatrists; and (b) since the available data indicate nurse practitioners achieve good outcomes and are more economically viable than psychiatrists, placement of psychiatric-mental health nurse practitioners in community settings should be recognized as a realistic solution to the shortfall of MH services.

 

Questions to consider:

1. How does the Affordable Care Act impact/improve mental health services?

2. What types of prevention and what types of intervention can provided through the Affordable Care Act?

3. What are some unintended outcomes for mental health services that could arise under the Affordable Care Act (positive or negative)?

 

Article 3:

 

Abstract:

The purpose of this study was to identify predictors of depressive symptoms among adolescents using concepts drawn from two theoretical models that underlie popular youth-focused programs. Specifically, we assessed the degree to which family-level risk factors increase the likelihood of depressive symptoms, and the degree to which community and/or school-level protective/promotive factors either buffer against risk, or directly lead to lower levels of depressive symptoms. Results indicate that three of the four hypothesized risk factors were associated with elevated levels of depressive symptoms. In addition, the protective/promotive factors had more promotive than protective effects because they were directly related to lower levels of symptoms. Implications for youth-focused programming are discussed.

 

Questions to consider:

1.   How does community and/or school-level protective/promotive factors buffer again the family-level risk factors for depressive symptoms?

2.   Which risk factors are most likely to be associated with elevated levels of depressive symptoms?

3.   What are the implications for youth programming?