Defining Mental Illness and Psychiatric Disability
Joel Michael Reynolds, Christine Wieseler in The Disability Bioethics Reader, 2022
The DSM has considerable influence on psychiatric practice throughout the world, but criticism has followed the manual since its inception. Over the decades, one repeated concern is that the DSM contributes to “disease mongering” – the creation of conditions to fit psychotherapeutics, whether with respect to their development or sales. The diagnostic categories within the DSM expand significantly and become more inclusive with each edition, so much so that current estimates are that half of Americans qualify for a DSM diagnosis at some point in their lives (Kawa and Giordano 2012, 7). As a related concern: “the ‘pathologization of deviance’ and the ‘medicalization of social ills’ are potential effects of psychiatric diagnoses and treatment trends” (ibid.). Each new edition of the DSM has moved increasingly toward biomedical modeling, including attempts to incorporate neuroimaging and genetics. Research and insurance reimbursement dollars have also increased accordingly (ibid., 6). Concerns about biased diagnoses, disease mongering, expansive pathologization, and biomedical modeling culminated in a social-political movement for a group of mental health professionals, current and would-be patients, and the general public.
Animal healers
Clive R. Hollin in An Introduction to Human–Animal Relationships, 2021
The condition known as autism has had several diagnostic revisions (it follows that over time researchers will use changing diagnoses). In 1994 the American Psychiatric Association (APA; DSM-IV) categorised autism as a spectrum ranging from mild to severe. To aid diagnosis, DSM-5 (APA, 2013) introduced the term autism spectrum disorder defined by two groups of symptoms, each consisting of specific behaviours, evident in early childhood: (1) persistent impairment in reciprocal social communication and social interaction; (2) restricted, repetitive patterns of behaviour. The psychological and social consequences of ASD are felt by both the individual and their family. Animals, principally dogs and horses but also dolphins, guinea pigs, llamas and rabbits (e.g., Griffioen, van der Steen, Cox, Verheggen, & Enders-Slegers, 2019; O’Haire, McKenzie, Beck, & Slaughter, 2019) have been incorporated into a plethora of different therapies. Several illustrative studies are described below.
Clinical Sequelae and Functional Outcomes
Mark A. Mentzer in Mild Traumatic Brain Injury, 2020
The severity of TBI is classified using the Glasgow Coma Scale (GCS), Loss of Consciousness (LOC), and post-traumatic amnesia (PTA), along with a variety of other screening tools such as ANAM (Automated Neuropsychological Assessment Metrics), the Repeatable Battery for Assessment of Neuropsychological Status, the Concussion Management Algorithm (CMA), the King-Devick concussion test (North et al., 2012; Marshall et al., 2012), the Sport Concussion Assessment Tool (SCAT3), and the Acute Concussion Evaluation (Ontario Neurotrauma Foundation, 2013; Gioia and Collins, 2006). And no single classification embraces all the features of mTBI (clinical, pathological, cellular/molecular). The severity does not directly equate to neurocognitive disorder (NCD) or the potential for rehabilitation. Many factors such as injury specifics, age, prior history, and substance abuse relate to the effects of a TBI (Relias Academy, 2020). Edition 5 of the Diagnostic and Statistical Manual for Mental Disorders (DSM-5) describes the neurocognitive sequelae following TBI. NCD encompasses the group of acquired disorders wherein the primary clinical deficit is disrupted cognitive functioning (American Psychiatric Association, 2013). DSM-5 is the standard classification of mental disorders used by mental health professionals in the United States.
The sensory processing patterns of individuals with schizophrenia with comorbid substance use disorder
Published in Journal of Substance Use, 2023
Gülşah Zengin, Meral Huri
Participants were consisted of individuals who have attended an inpatient or outpatient treatment program in a psychiatry clinic for more than three months. SG were included in the study if they met the following criteria: (a) having schizophrenia and substance use disorder according to DSM-5 criteria, (b) being in the age range of 25–45, (c) being in a stable clinical condition, (d) being literate. CG were eligible if meeting the following inclusion criteria: (a) having schizophrenia according to DSM-5 criteria, (b) being in the age range of 25–45, (c) being in a stable clinical condition, (d) being literate. Both groups were included exclusion criteria if they secondary mental health disorder (bipolar affective disorder, depression, etc.) and have or are receiving prior sensory processing interventions.
A Crosswalk Study of DSM-IV and DSM-5 Criteria for PTSD from the DSM-5 Field Trials
Published in Psychiatry, 2022
Carol S. North, Alina M. Surís, Diana Clarke, Jayme M. Palka, Lamyaa Yousif, Darrel A. Regier
Although DSM-III-R and DSM-IV made extensive changes to the PTSD criteria, DSM-5 made even more revisions (North et al., 2016). The two most substantive changes from DSM-IV to DSM-5 were: 1) removal of the A2 criterion that required an individual reaction involving intense fear, helplessness, or horror and 2) increase in the number of symptom groups from 3 to 4 (by splitting the DSM-IV avoidance/numbing symptom group to create an avoidance-only group, absorbing the numbing symptoms into a new symptom group for negative alternations in cognitions and mood criteria, to yield criteria B-D in DSM-IV and B-E in DSM-5; Stein et al., 2014). Many other changes to the criteria in DSM-5 included addition of an exposure category (repeated or extreme exposure to aversive details of trauma, largely for experiences of workers in certain occupations), removal and addition of other symptoms, a new requirement that the disorder is not due to physiological effects of substances or medical conditions, rewording of criteria, and clarification of criteria in the accompanying text, as described as in detail elsewhere (North et al., 2016, 2021).
An insight into diagnosis of depression using machine learning techniques: a systematic review
Published in Current Medical Research and Opinion, 2022
Sweta Bhadra, Chandan Jyoti Kumar
Depression is a leading cause of disability worldwide and it contributes significantly to the global burden of disease1. The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) characterizes depression by persistent sadness, lack of interest, change in appetite, sleep disturbances, fatigue, suicidal thoughts and lack of decisiveness2. The symptoms can be long-lasting and can have a significant impact on a person's well-being as well as his ability to work3. Studies have proven that there is a close relationship between depression and physical health4. Depression may cause cardiovascular diseases, diabetes mellitus and in the worst-case scenario, it can even lead to suicide2,5,6. According to the World Health Organization (WHO), over 264 million people are affected by depression globally3. Among them, persons with major depressive disorder (MDD) show a very high-risk rate for committing suicide (∼15%). It is highly alarming as suicide is an extremely complex phenomenon that incorporates widely varied mechanisms and demands careful assessment and rigorous clinical interventions7. Figure 1. depicts the top ten countries having the highest prevalence rate of depression.
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