A mixed-methods investigation into the perspectives on mental health and professional treatment among former system youth with mood disorders.
Michelle R MunsonSarah Carter NarendorfShelly Ben-DavidAndrea ColePublished in: The American journal of orthopsychiatry (2018)
Research has shown that how people think about their health (or illnesses) shapes their help-seeking behavior. In this mixed-methods study, we employed a simultaneous concurrent design to explore the perceptions of mental illness among an understudied population: marginalized young adults. Participants were 60 young adults (ages 18-25) who had experienced mood disorders and used multiple public systems of care during their childhoods. Semistructured interviews were conducted to understand participants' illness and treatment experiences during the transition to adulthood. A team of analysts used constant comparison to develop a codebook of the qualitative themes, and quantitative data were examined using SAS 9.3. Findings suggest that some theoretical categories identified in past illness-perceptions frameworks are salient to marginalized young adults (e.g., identity, management-or control-of symptoms), but both the developmental transition to adulthood and experiences with public systems of care add nuanced variations to illness and treatment perceptions. Our study demonstrates that young adults possess a set of beliefs and emotions about their mental health and help-seeking options that need to be better understood to improve engagement and quality of mental health care for this population. Implications for practice, research, and policy are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Keyphrases
- mental health
- young adults
- mental illness
- healthcare
- primary care
- palliative care
- quality improvement
- bipolar disorder
- public health
- randomized controlled trial
- squamous cell carcinoma
- physical activity
- sleep quality
- radiation therapy
- clinical trial
- childhood cancer
- emergency department
- artificial intelligence
- risk assessment
- social media
- machine learning