What kind of science for dual diagnosis? A pragmatic examination of the enactive approach to psychiatry

Multi-professional healthcare teams, medical dominance, and institutional epistemic injustice

Multi-professional teams have become increasingly common in healthcare. Collaboration within such teams aims to enable knowledge amalgamation across specializations and to thereby improve standards of care for patients with complex health issues. However, multi-professional teamwork comes with certain challenges, as it requires successful communication across disciplinary and professional frameworks. In addition, work in multi-professional teams is often characterized by medical dominance, i.e., the perspective of physicians is prioritized over those of nurses, social workers, or other professionals. We argue that medical dominance in multi-professional teams can lead to institutional epistemic injustice, which affects both providers and patients negatively. Firstly, it codifies and promotes a systematic and unfair credibility deflation of the perspectives of professionals other than physicians. Secondly, it indirectly promotes epistemic injustice towards patients via leading to institutional opacity; i.e., via creating an intransparent system of credibility norms that is difficult to navigate. To overcome these problems, multi-professional teamwork requires institutional settings that promote epistemic equity of team members.

Crisis of objectivity: using a personalized network model to understand maladaptive sensemaking in a patient with psychotic, affective, and obsessive-compulsive symptoms

Introduction

Psychiatric comorbidities have proven a consistent challenge. Recent approaches emphasize the need to move away from categorical descriptions of symptom clusters towards a dimensional view of mental disorders. From the perspective of phenomenological psychopathology, this shift is not enough, as a more detailed understanding of patients' lived experience is necessary as well. One phenomenology-informed approach suggests that we can better understand the nature of psychiatric disorders through personalized network models, a comprehensive description of a person's lifeworld in the form of salient nodes and the relationships between them. We present a detailed case study of a patient with multiple comorbidities, maladaptive coping mechanisms, and adverse childhood experiences.

Methods

The case was followed for a period of two years, during which we collected multiple streams of data, ranging from phenomenological interviews, neuropsychological assessments, language analysis, and semi-structured interviews (Examination of Anomalous Self Experience and Examination of Anomalous World Experience). We analytically constructed a personalized network model of his lifeworld.

Results

We identified an experiential category "the crisis of objectivity" as the core psychopathological theme of his lifeworld. It refers to his persistent mistrust towards any information that he obtains that he appraises as originating in his subjectivity. We can developmentally trace the crisis of objectivity to his adverse childhood experience, as well as him experiencing a psychotic episode in earnest. He developed various maladaptive coping mechanisms in order to compensate for his psychotic symptoms. Interestingly, we found correspondence between his subjective reports and other sources of data.

Discussion

Hernan exhibits difficulties in multiple Research Domain Criteria constructs. While we can say that social sensorimotor, positive valence, and negative valence systems dysfunctions are likely associated with primary deficit (originating in his adverse childhood experience), his cognitive symptoms may be tied to his maladaptive coping mechanisms (although, they might be related to his primary disorder as well).

Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach

Significant clinical overlap exists between mental health and substance use disorders, especially among women. The purpose of this research is to leverage an AutoML (Automated Machine Learning) interface to predict and distinguish co-occurring mental health (MH) and substance use disorders (SUD) among women. By employing various modeling algorithms for binary classification, including Random Forest, Gradient Boosted Trees, XGBoost, Extra Trees, SGD, Deep Neural Network, Single-Layer Perceptron, K Nearest Neighbors (grid), and a super learning model (constructed by combining the predictions of a Random Forest model and an XGBoost model), the research aims to provide healthcare practitioners with a powerful tool for earlier identification, intervention, and personalised support for women at risk. The present research presents a machine learning (ML) methodology for more accurately predicting the co-occurrence of mental health (MH) and substance use disorders (SUD) in women, utilising the Treatment Episode Data Set Admissions (TEDS-A) from the year 2020 (n = 497,175). A super learning model was constructed by combining the predictions of a Random Forest model and an XGBoost model. The model demonstrated promising predictive performance in predicting co-occurring MH and SUD in women with an AUC = 0.817, Accuracy = 0.751, Precision = 0.743, Recall = 0.926 and F1 Score = 0.825. The use of accurate prediction models can substantially facilitate the prompt identification and implementation of intervention strategies.

Navigating the Complex Intersection of Substance Use and Psychiatric Disorders: A Comprehensive Review

The co-occurrence of substance use disorders (SUDs) and psychiatric conditions, often referred to as comorbidity or concurrent disorders, presents intricate challenges in both diagnosis and treatment. This comprehensive narrative review aims to synthesize and critically evaluate the existing evidence surrounding the management of individuals with comorbid SUDs and psychiatric disorders. Comorbidity in these domains carries profound implications for clinical practice, research, and policymaking, emphasizing the need for a holistic understanding of the intricate dynamics that arise when these conditions coexist. This review explores recent research findings, evidence-based guidelines, and emerging trends within the field, offering valuable insights for clinicians, researchers, and policymakers seeking to navigate the complex terrain of comorbidity in substance use and psychiatric disorders.