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The myth of reliability of DSM

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Abstract

Challenges the conventional wisdom about the research data used to support the claim by the authors of the Diagnostic and Statistical Manual of Mental Disorders-III (DSM-III) that the DSM-III's development was guided by scientific principles and evidence and that its innovative approach to diagnosis greatly ameliorated the problem of the unreliability of psychiatric diagnoses. This paper argues that the rhetoric of science, more than the scientific data, was used convincingly by the developers of DSM-III to promote their new manual. Data gathered in the original DSM-III field trials are reanalyzed in light of interpretations that had been offered earlier for other reliability studies. Results demonstrate how standards for interpreting reliability were dramatically shifted over time in a direction that made it easier to claim success with DSM-III when, in fact, the data were equivocal. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
... Briefly, these trait domains are negative affectivity (e.g., hostility, emotional liability), detachment (e.g., withdrawal, anhedonia), antagonism (e.g., manipulativeness, grandiosity), disinhibition (e.g., impulsivity, rigid perfectionism), and psychoticism (e.g., eccentricity, unusual beliefs and experiences) (American Psychiatric Association, 2013a). This trait-based model was developed to address the long-standing issues with the categorical model for reasons related to outdated/arbitrary thresholds (Krueger, 2019;Widiger and Trull, 2007), resulting in a lack of reliability (Chmielewski et al., 2015;Kirk and Kutchins, 1994;Vanheule, 2014) and excessive diagnostic comorbidity (Pulay et al., 2009;Stuart et al., 1998;Widiger and Trull, 2007). The categorical model also demonstrated an inadequate coverage of personality pathology, as evidenced by the significant overuse of the "not otherwise specified" diagnosis. ...
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The current study investigates the relationship between insecure attachment and pathological personality trait domains in a sample of psychiatric outpatients. Participants (N = 150) completed measures for attachment and personality. Bivariate correlations and multiple regression analyses investigated the extent to which insecure attachment and personality pathology were associated. Insecure attachment positively correlated with overall personality pathology, with attachment anxiety having a stronger correlation than attachment avoidance. Distinct relationships emerged between attachment anxiety and negative affectivity and attachment avoidance and detachment. Insecure attachment and male sex predicted overall personality pathology, but only attachment anxiety predicted all five trait domains. Insecure attachment might be a risk factor for pathological personality traits. Assessing attachment in clinical contexts and offering attachment-based interventions could benefit interpersonal outcomes.
... Responding to a perceived lack of conceptual clarity may lead to 'the substitution of precision for validity' (Kirk & Kutchins, 1994cited in Bowker & Star, 1999. For example, there are many warnings against quantification as a solution to conceptual confusion, on the grounds that quantification has exacerbated 'conceptual stretching', by 'switching from "what is" questions to "how much" questions' (Sartori, 1970(Sartori, , p. 1036(Sartori, , 1039. ...
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Co-production refers to a reciprocal process of exchange between diverse stakeholders, in order to generate outcomes that are only possible because of this deliberate intersection of difference. Whilst the concept of co-production appeals within and for futures studies, foresight and anticipatory politics, its conceptual messiness has been widely critiqued. Drawing upon an integrative literature review of co-production and concept formation in the social sciences, we identify three approaches that scholars of co-production have sought to mobilise in order to address this critique. Each approach offers a different perspective on what makes a ‘good’ social scientific concept: clarification, elucidation and provocation. Our analysis illuminates the value of holding different approaches to conceptualisation in tension, as a means of developing a richer and more contingent understanding of co-production to future studies’ debates. In doing so, we open up new conceptual imaginaries for co-production and its prefigurative value within futures studies, offering more pluralistic ways of knowing in a context of radical uncertainty
... Additionally, the strength of each of the editions of DSM has been "reliability" -the weakness is its lack of validity . It was even argued that the rhetoric of science, more than the scientific data, was used convincingly by the DSM developers to promote their manual, which back then was the DSM-III (Kirk, & Kutchins, 1994). ...
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Background: The structure of psychopathology determines how we identify people who need support services and how we can best help them. Currently, we identify those with psychopathological issues via assessments based on diagnostic manuals, such as the International Statistical Classification of Diseases (ICD) and Diagnostic and Statistical Manual of Mental Disorders (DSM). However, there is a growing literature that has raised serious concerns about these two manuals. Some have suggested that such diagnostic manuals have misguided decades of mental health studies and may have contributed to dissatisfaction among service seekers and users relating to ineffective treatments and negative experiences with service providers. This doctoral dissertation explores possible alternative approaches to our understanding of the structure of psychopathology. It considers how these approaches could contribute to future classification, diagnostic and service delivery systems. Method: We used one dataset for all four studies. It was mined from https://www.livejournal.com/ and consisted of narratives about lived experiences from people diagnosed with mental disorders. The data were analysed using Jaccard’s Coefficient to find similarity between diagnostic categories (study 1), K-Means Clustering to group symptoms into diagnostic categories (in study 2), Network Analysis to find the relationships between the co-occurring symptoms and Eigenvector Centrality to estimate which among them are co-occurring with most other symptoms (study 3), and standard correlation to find the strength of such associations (in study 4). Findings: We proposed an alternative approach for estimating the reliability of the existing system (study 1) to study the extent of diagnostic overlap (heterogeneity) because the present studies evaluating the reliability had their limitations. Study 1 (chapter 4) contributes to the literature by being the first study to exploit patient narrative data, using innovative text-mining methods in this context, to assess the diagnostic heterogeneity of the DSM categories. It provides unique evidence to reinforce existing studies of diagnostic heterogeneity using alternative approaches such as Jaccard’s coefficients. Once verified that the diagnostic heterogeneity of human-led traditional diagnostic categories is too large for practical usage, we searched for the reasons. Many studies have attributed the problem to the committee members who created the manuals. Among the several raised questions, the committee members reported a financial conflict of interests with the industry and relied more on consensus than data. So, eliminating the human component of decision making, we should be able to find homogeneous groups of disorders. Therefore, we attempted to create categories of mental illnesses using Artificial Intelligence (study 2) from patients’ reported symptoms. Study 2 (chapter 5) contributes to the literature by being the first study in this context to demonstrate how to cluster the patients using artificial intelligence based on the similarities in their reported symptoms or experiences from their illness narratives. It provides evidence to contrast the conventional idea of conceptualising “mental illnesses as categories” using unsupervised machine learning algorithms and the silhouette score elbow method. For example, in study 2, when the machine-driven approach also produced mental disorder categories with high heterogeneity, we inferred that while there might have been human biases with the traditional diagnostic manuals, the more important point is that the categorical approach is not the way forward. The findings from study 2 support the literature and state the same. The literature has proposed several alternatives, such as the dimensional and network approaches. But related to this notion of diagnosing and studying humans (and their conditions) as categories, such as depression, consisting of individual entities (e.g., symptoms), there is another serious problem with the mental health research culture - that has found its way into these new alternatives as well. This problem is related to using total scores of survey items as objects of inquiry (e.g., total depression score). This approach assumes that all the items in the questionnaire (e.g., low mood, lack of interest) contributes in equal proportions to the construct (e.g., depression), but the empirical evidence suggests otherwise. The newer dimensional approaches such as the HiTOP relies on such sum scores. Likewise, some network studies are also using such sum scores. Therefore, in doing so, such alternatives risk carrying forward some of the weaknesses of its categorical predecessors. As an alternative, we proposed the use of individual symptoms as an object of inquiry. It’s a relatively novel approach, and we hoped to advance the literature. Therefore, we created a network of psychopathological symptoms based on patients’ reports (study 3). Study 3 (chapter 6) contributes to the literature by being the first study to demonstrate how to create network graphs from pure narrative data from patients in this context and presented a new approach for exploratory analysis by finding inter-relations in their reported symptoms or experiences from patients’ illness narratives. It demonstrates a relatively novel approach to focus on individual symptoms for the object of inquiry instead of categories of mental disorders or sum-scores of scales or questionnaires. The study discovered relationships based on co-occurrences of the reported symptoms. Still, it did not communicate the strength (“numeric” degree) of such association. While finding the association has merit for preliminary exploration, for this approach of using individual symptoms as an object of inquiry to be useful for clinical and research purposes, we argue that it must provide the information related to the strength of association. So, in the final study, we attempted to find the correlations of auditory hallucination and, in doing so, demonstrated how to find correlation coefficients between pairs of symptoms from a qualitative (text-based narrative) dataset. Furthermore, the correlations were valuable to the advancement of the theoretical literature of auditory hallucination. Study 4 (chapter 7) contributes to the literature by being the first study to demonstrate how to do correlation analysis on qualitative data in this context. It suggests a new direction of conducting exploratory research using rich qualitative datasets and standard statistical methods without the limitations of a conventional survey dataset. Conclusion: The doctoral thesis found that the traditional categorical approach does not accurately reflect the complexity of people’s experiences. There might be human biases and conflict of interest, which might have influenced the creation of the diagnostic manuals. Still, even when artificial intelligence attempted to find similar patterns within the patients’ experiences, it could not indicate that psychopathological experiences cannot be categorised into homogenous groups. So, we argue that the future of mental health literature should divorce itself from using DSM and ICD categories of mental disorders as the object of investigations and as the framework for conceptualising mental illnesses. Instead, we argue that the focus should be on alternative conceptualisations of psychopathology, such as the network model of psychopathology, which focuses on the individual symptoms and the inter-relationships between them. Our preliminary network model explores the specific relationships between symptoms found that were frequently occurring but relatively less studied in the literature - opening up newer lines of investigation for future studies to build upon. Furthermore, using auditory hallucination as an object of investigation, we found the variables with the highest correlation coefficients and attempted to advance the psychosis literature. One major merit and contribution of the doctoral thesis is to demonstrate how we can do all that was mentioned above using rich qualitative data. Unlike survey data, the current data did not pose any restrictions in terms of the number or type of variables being reported. The respondents reported everything that had to report. Additionally, the thesis demonstrated how a large volume of qualitative data could be obtained and then analysed using statistical and machine learning-based approaches with minimum effort and time using advanced technologies such as Natural Language Processing, Artificial Intelligence, and Web-Scraping technologies. This thesis's second major merit and contribution are to demonstrate how to use novel data analytic procedures such as Jaccard’s Coefficient, K-Means Clustering and Network Graphs, and conventional statistics such as correlation coefficients on such qualitative datasets. No manual analysis, such as thematic analysis of the qualitative data, was done. This thesis's third merit and contribution were in terms of advancing the literature by evaluating diagnostic heterogeneity between categories of mental disorders using a novel approach (study 1); finding out symptoms that exclusive to each cluster of mental disorders (study 2); estimating the tendency of specific symptoms to co-occur with other symptoms (study 3), and finding out the symptoms associated with auditory hallucination (study 4). Future mental health studies will benefit from this contribution and are expected to produce deeper insight into mental conditions and treatment of mental ill-health.
... There is an emerging literature suggesting that the traditional diagnostic systems such as the Diagnostic and Statistical Manual of Mental Disorders (DSM-5, [6]) and the International Classification of Diseases 11th Revision (ICD-11, World Health Organization, WHO, 2020) [7] are unreliable (e.g., [1,8,9]). The same service seeker might receive two different diagnoses by two independent clinicians (i.e., low inter-rater reliability). ...
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Background To deliver appropriate mental healthcare interventions and support, it is imperative to be able to distinguish one person from the other. The current classification of mental illness (e.g., DSM) is unable to do that well, indicating the problem of diagnostic heterogeneity between disorders (i.e., the disorder categories have many common symptoms). As a result, the same person might be diagnosed with two different disorders by two independent clinicians. We argue that this problem might have resulted because these disorders were created by a group of humans (APA taskforce members) who relied on more intuition and consensus than data. Literature suggests that human-led decisions are prone to biases, group-thinking, and other factors (such as financial conflict of interest) that can enormously influence creating diagnostic and treatment guidelines. Therefore, in this study, we inquire that if we prevent such human intervention (and thereby their associated biases) and use Artificial Intelligence (A.I.) to form those disorder structures from the data (patient-reported symptoms) directly, then can we come up with homogenous clusters or categories (representing disorders/syndromes: a group of co-occurring symptoms) that are adequately distinguishable from each other for them to be clinically useful. Additionally, we inquired how these A.I.-created categories differ (or are similar) from human-created categories. Finally, to the best of our knowledge, this is the first study, that demonstrated how to use narrative qualitative data from patients with psychopathology and group their experiences using an A.I. Therefore, the current study also attempts to serve as a proof-of-concept. Method We used secondary data scraped from online communities and consisting of 10,933 patients’ narratives about their lived experiences. These patients were diagnosed with one or more DSM diagnoses for mental illness. Using Natural Language Processing techniques, we converted the text data into a numeric form. We then used an Unsupervised Machine Learning algorithm called K-Means Clustering to group/cluster the symptoms. Results Using the data mining approach, the A.I. found four categories/clusters formed from the data. We presented ten symptoms or experiences under each cluster to demonstrate the practicality of application and understanding. We also identified the transdiagnostic factors and symptoms that were unique to each of these four clusters. We explored the extent of similarities between these clusters and studied the difference in data density in them. Finally, we reported the silhouette score of + 0.046, indicating that the clusters are poorly distinguishable from each other (i.e., they have high overlapping symptoms). Discussion We infer that whether humans attempt to categorise mental illnesses or an A.I., the result is that the categories of mental disorders will not be unique enough to be able to distinguish one service seeker from another. Therefore, the categorical approach of diagnosing mental disorders can be argued to fall short of its purpose. We need to search for a classification system beyond the categorical approaches even if there are secondary merits (such as ease of communication and black-and-white (binary) decision making). However, using our A.I. based data mining approach had several meritorious findings. For example, we found that some symptoms are more exclusive or unique to one cluster. In contrast, others are shared by most other clusters (i.e., identification of transdiagnostic experiences). Such differences are interesting objects of inquiry for future studies. For example, in clear contrast to the traditional diagnostic systems, while some experiences, such as auditory hallucinations, are present in all four clusters, others, such as trouble with eating, are exclusive to one cluster (representing a syndrome: a group of co-occurring symptoms). We argue that trans-diagnostic conditions (e.g., auditory hallucinations) might be prime targets for symptom-level interventions. For syndrome-level grouping and intervention, however, we argue that exclusive symptoms are the main targets. Conclusion Categorical approach to mental disorders is not a way forward because the categories are not unique enough and have several shared symptoms. We argue that the same symptoms can be present in more than one syndrome, although dimensionally different. However, we need additional studies to test this hypothesis. Future directions and implications were discussed.
... Elle soumet ainsi le diagnostic au biais de Berkson, c'est-à-dire la 6 tendance à sous-estimer le pronostic fonctionnel de ces patients, en diagnostiquant les formes plus chroniques et graves (Guloksuz & van Os, 2018). L'approche catégorielle de la schizophrénie du DSM ou de la CIM, sous forme de "liste de symptômes" est donc un modèle idéalement fiable en termes de concordance inter-juge (Frances, 2012;Kirk & Kutchins, 1994) (Husserl, 2015), la phénoménologie se donne pour objet l'étude des phénomènes. Elle entend chez Husserl, par la méthodologie de l'épochè (Husserl, 1913), revenir aux choses elles-mêmes, c'est-à-dire d'observer le 8 rapport de corrélation entre la conscience et ses objets. ...
Thesis
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Il est admis depuis quelques décennies que la schizophrénie est une pathologie mentale sévère de laquelle il est pourtant possible de se rétablir. Le rétablissement, notamment dans sa composante expérientielle, s’appuie sur les aspects subjectifs de l’expérience schizophrénique, bien étudiés par la littérature phénoménologique. Dans le cadre des soins orientés vers le rétablissement, Paul H. Lysaker a fondé un modèle de psychothérapie individuelle qui s’appuie sur l’approche métacognitive de la schizophrénie, concevant la pathologie mentale sévère comme une interruption du sens de soi. Il n’existe pas à ce jour de modèle théorique permettant de penser une psychothérapie collective visant le rétablissement expérientiel. Afin de répondre à cette lacune, notre travail se propose de tracer des voies d’accès conceptuelles, à partir d’outils théoriques empruntés à la psychopathologie phénoménologique et à l’approche métacognitive. Ces outils, articulés autour des concepts de centralité et de décentration, nous ont permis de penser la psychothérapie collective comme milieu métacognitif. ............................................................................................................................................................................................................................................................ It has been accepted for some decades that schizophrenia is a severe mental illness from which it is possible to recover. Recovery, particularly in its experiential component, is based on the subjective aspects of the schizophrenic experience, well studied by the phenomenological literature. In the context of recovery oriented care, Paul H. Lysaker founded a model of individual psychotherapy based on the metacognitive approach to schizophrenia, conceiving severe mental pathology as an interruption of the sense of self. To date, there is no theoretical model for thinking about collective psychotherapy aimed at experiential recovery. In order to respond to this gap, our work proposes to trace conceptual pathways, using theoretical tools borrowed from phenomenological psychopathology and the metacognitive approach. These tools, articulated around the concepts of centrality and decentration, have allowed us to think of collective psychotherapy as a metacognitive milieu.
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Behavior analysis, once a promising approach to understanding and treating severe mental disorders, has been obscured by the biomedical model of mental illness and its ubiquitous psychotropic drugs. The present paper gives a brief overview of behavioral research on psychotic disorders followed by a critical review of the prevailing biomedical model including psychiatric diagnoses, anti-psychotic medications, clinical outcomes, and adverse effects of drug treatment. This paper also examines ideological, political, and economic mechanisms of control that have blocked the application of behavior analysis with severe mental disorders.
Chapter
Decades of research show that psychosocial treatments are effective for psychosis, yet they remain unimplemented as the American healthcare system relies primarily on pharmacological solutions instead. This book reviews the history and current state of research to provide a more nuanced understanding of the evidence for and barriers to psychosocial care for psychosis. It addresses a wide range of mental health research and multi-professional practice domains from historical, personal, societal, professional, and systems perspectives. The varied perspectives presented illustrate factors that limit support for recovery in SMI and psychosis as well as real hope for recovering the US mental healthcare system. With contributions of experts by training and by experience, this book represents an essential resource for students, practitioners and researchers.
Chapter
Psychology is a culture-based scientific discipline and clinical practice. In the past decades, Western theories and methods have played a dominant role in the psychological science and practice development in China. The time has come that psychology of China (vs. psychology in China) rooted in the Chinese culture be recognized and respected, and further developed that centers Chinese cultural values and well-being. To do so, we argue that a paradigm shift is necessary and urgent, which involves questioning the existing Western paradigms that have permeated every aspect of modern psychology and revitalizing traditional Chinese cultural values and psychological wisdom. The exiting Western empiricism in science needs to be replaced by methodological holism with a culture-centered hermeneutic approach, the exclusive individualism in theory by epistemological pluralism, and the Western medical model by Eastern philosophical/cultural models in psychotherapy. Psychology of China is not only possible but inevitable and it starts with a paradigm shift.KeywordsParadigm shiftPsychologyCulturePluralismChinese philosophy
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