Clinical psychology is the branch of psychology concerned with conditions that negatively impact a person’s daily life, from depression to schizophrenia. Clinical psychology data provides resources that enable sufferers, their families, and others to better manage, understand, and treat these conditions.
Most clinical psychology data comes from medical and hospital records or from experiments and studies published in research journals. However, for a number of reasons, diagnosis of psychological disorders lags behind the true prevalence in the population, so some of this data must come from other sources, like suicide rate or economic and demographic traits.
However, sufferers also provide invaluable information through various means. They not only provide answers to scientific surveys, but also through personal writings and even artistic endeavors. Certainly, mood and self-conception can drastically impact a person’s artistic output but researchers have also found they can diagnose people using art. A tree-drawing test, for example, has been used since the early 1950’s to diagnose emotional disturbances in a number of different cultures.
Historical records can also provide important information on psychiatric conditions and their treatments.
There is a vast amount of clinical psychology data available on all kinds of conditions. Data can be divided into data about diagnoses, about treatment, about drug development, and about people diagnosed with these conditions.
Data about diagnoses includes standards and qualifications for diagnoses and, as noted above, estimates of the rates of mental illness within a population through other means. For example, South Korea has one of the highest rates of suicide in the world, yet data from official health records do not show a proportionate rate of depression diagnoses. This data also includes historical information on changing diagnosis criteria or the addition and removal of diseases. There is even cross-cultural data to investigate, as some conditions only manifest in certain cultures.
Treatment and drug development data are similar in that they measure the efficacy of occupational, personal, and drug treatments on patient outcomes. Workplace accommodations, family and community connections, and lifestyle as well as in-patient and out-patient treatment protocols are well-studies data points. Drug development and clinical trial data also provide very important information for clinicians and patients.
Finally, researchers will find patient data, including demographics, behavior and lifestyle (including coping mechanisms), and comorbities.
Uses for this data are, first, to find cures or better management of abnormal conditions. Clinicians, public health officials, and others also use this data to ensure individual patients and larger populations receive needed care. At times, the government may need to step in—for example, by direct more funding to research studies or imposing stricter regulations on prescription drugs.
There are also commercial uses for this data, as many companies provide medical devices, supplies, or drugs to clinics. Some companies also market products directly to patients: for example symptom-management apps have found great success among the general public. Clinicians and general practitioners also find these products helpful, as they can use the information—often collected by wearable devices—to improve patient treatment plans.
As noted above, most of this data comes from clinical settings and research organizations. It can therefore be considered high quality. However, not all data is of the same quality. Some studies, for example, may not be able to recruit a large sample size of people with certain conditions.
Finally, the nature of clinical psychology makes diagnosis itself quite inaccurate at times. For instance, diagnostic manuals undergo revisions that add or remove entire conditions; sufferers misdiagnosed in one clinic go on to find correct diagnoses in another; recording accurate information on home life can seem nearly impossible; and so on. In short, researchers should not assume the accuracy of any dataset, especially from previous diagnostic eras.
The advent of computational psychiatry — comparing a computer-simulated model of normal brain processes to dysfunctional processes observed in tests — could be an important supplement to the diagnostic process for ADHD, the Ohio State researchers report in a new review published in the journal Psychological Bulletin.
The research team reviewed 50 studies of cognitive tests for ADHD and described how three common types of computational models could supplement these tests.
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