Abstract:
Mental health diagnoses serve to aid clinicians in determining the most effective treatment plans
for patients, estimating prognoses, and communicating information between parties in different
settings (for example, between counselors, doctors, and billing companies). To say accurate
mental health diagnosis saves lives is no exaggeration: psychiatric prognoses predict
symptomology, which can include severe risk factors such as suicidality, and are instrumental in
the establishment of coping mechanisms and treatment options. In more ways than one, the
current diagnostic system (the Diagnostic Statistical Manual of Mental Disorders, or DSM) fails
to capture complexity of mental illness and foregoes case-by-case analysis in favor of a rigid
categorical approach, comprised of categories that are relatively unsupported by research. No
issue highlights these shortcomings quite like the climbing prevalence of comorbid diagnoses, or
simultaneous diagnoses of more than one mental disorder, which leads to the complication of
treatment plans and muddies the field’s understanding of psychopathology. In a dimensional
model, however, this could be improved. I analyze role of comorbidity in diagnosis and the
hierarchical nature of mental illness, as well as the existence of mental disorders on a continuum
with normalcy (rather than black and white assessments of “afflicted” or “normal”). I also
propose a dimensional alternative rooted in empirical evidence and suggest measures required to
incorporate the model into clinical practice.
