'The Explanation You Have Been Looking For': Neurobiology as Promise and Hermeneutic Closure

Chemical imbalance and etiological beliefs about depression among college students

Etiological beliefs of depression have differing impacts on motivation, hope, and treatment expectations. However, it is unclear where people are exposed to these beliefs. Objective: This study examined beliefs about depression and their relations to symptoms, attitudes about depression, and treatment preferences. Participants: 426 undergraduates attending a large midwestern university. Methods: Participants completed an online survey asking about causes of depression, if and where they had heard about the "chemical imbalance" explanation of depression, attitudes about depression, as well as measures of their symptoms, treatment history, and hypothetical treatment preferences. Results: Sixty-two percent of the sample had heard of the chemical imbalance explanation, most commonly from the classroom. Biochemical beliefs about depression were most strongly endorsed among participants with a family history of depression and who had had personal experience with treatment. The chemical imbalance belief was uniquely related to dysfunctional beliefs about depression. Etiological beliefs were largely unrelated to treatment preferences. Conclusion: College students are exposed to models of mental health that may not be ideal for treatment and recovery.

Patient Perceptions of Illness Causes and Treatment Preferences for Obsessive-Compulsive Disorder: A Mixed-Methods Study

Obsessive-compulsive disorder (OCD) is a condition with high patient morbidity and mortality. Research shows that eliciting patient explanations about illness causes and treatment preferences promotes cross-cultural work and engagement in health services. These topics are in the Cultural Formulation Interview (CFI), a semi-structured interview first published in DSM-5 that applies anthropological approaches within mental health services to promote person-centered care. This study focuses on the New York City site of an international multi-site study that used qualitative-quantitative mixed methods to: (1) analyze CFI transcripts with 55 adults with OCD to explore perceived illness causes and treatment preferences, and (2) explore whether past treatment experiences are related to perceptions about causes of current symptoms. The most commonly named causes were circumstantial stressors (n = 16), genetics (n = 12), personal psychological traits (n = 9), an interaction between circumstantial stressors and participants' brains (n = 6), and a non-specific brain problem (n = 6). The most common treatment preferences were psychotherapy (n = 42), anything (n = 4), nothing (n = 4), and medications (n = 2). Those with a prior medication history had twice the odds of reporting a biological cause, though this was not a statistically significant difference. Our findings suggest that providers should ask patients about illness causes and treatment preferences to guide treatment choice.

Pharmaceuticalization and Care Coordination in New York City Outpatient Mental Health

US government quality measures prioritize pharmaceuticalization and care coordination to promote patient treatment adherence. How these measures affect outpatient mental health service delivery and patient-provider communication where psychiatrists and nonphysicians collaborate is understudied. Analyzing 500 hours of participant-observation, 117 appointments, and 98 interviews with 45 new patients and providers, I show that psychiatrists and social workers coordinated care by encouraging medications and seeing two mental health providers as the default treatment, irrespective of patient preferences. Ethnographic perspectives crucially account for models of service delivery and provider behaviors in researching treatment adherence.

Is the mainstream construction of mood disorders resistant to systemic thinking?

Introduction

In this study we explore how the diagnostic category of mood disorders is constructed in two handbooks of Psychopathology as an example of the mainstream construction of psychopathology. Despite the increasing criticism and lack of evidence, the debunked chemical imbalance theory of the etiology of depression still dominates the professional and pop/folk understanding and interventions.

Methods

We analysed the breadth of the inference field and the type of etiopathogenetic contents of the explanations of mood disorders using the "1to3" Coding System.

Results

Our findings show that the dominant explanations draw almost exclusively onto monadic explanations, followed by limited dyadic ones. Intrapersonal etiopathogenetic contents prevailed, and biomedical explanations were dominant in both textbooks.

Discussion

We critically discuss the underpinnings of these results and address the clinical implications of these biased representations, as well as potential alternative approaches to psychopathology.

The Influence of Using Novel Predictive Technologies on Judgments of Stigma, Empathy, and Compassion among Healthcare Professionals

BACKGROUND

Our objective was to evaluate whether the description of a machine learning (ML) app or brain imaging technology to predict the onset of schizophrenia or alcohol use disorder (AUD) influences healthcare professionals' judgments of stigma, empathy, and compassion.

METHODS

We randomized healthcare professionals (N = 310) to one vignette about a person whose clinician seeks to predict schizophrenia or an AUD, using a ML app, brain imaging, or a psychosocial assessment. Participants used scales to measure their judgments of stigma, empathy, and compassion.

RESULTS

Participants randomized to the ML vignette endorsed less anger and more fear relative to the psychosocial vignette, and the brain imaging vignette elicited higher pity ratings. The brain imaging and ML vignettes evoked lower personal responsibility judgments compared to the psychosocial vignette. Physicians and nurses reported less empathy than clinical psychologists.

CONCLUSIONS

The use of predictive technologies may reinforce essentialist views about mental health and substance use that may increase specific aspects of stigma and reduce others.

Evidence, ethics and the promise of artificial intelligence in psychiatry

Researchers are studying how artificial intelligence (AI) can be used to better detect, prognosticate and subgroup diseases. The idea that AI might advance medicine's understanding of biological categories of psychiatric disorders, as well as provide better treatments, is appealing given the historical challenges with prediction, diagnosis and treatment in psychiatry. Given the power of AI to analyse vast amounts of information, some clinicians may feel obligated to align their clinical judgements with the outputs of the AI system. However, a potential epistemic privileging of AI in clinical judgements may lead to unintended consequences that could negatively affect patient treatment, well-being and rights. The implications are also relevant to precision medicine, digital twin technologies and predictive analytics generally. We propose that a commitment to epistemic humility can help promote judicious clinical decision-making at the interface of big data and AI in psychiatry.