Faculty Bibliography

Large language models (LLMs) offer promising applications in mental health care to address gaps in treatment and research. By leveraging clinical notes and transcripts as data, LLMs could improve diagnostics, monitoring, prevention, and treatment of mental health conditions. However, several challenges persist, including technical costs, literacy gaps, risk of biases, and inequalities in data representation. In this Viewpoint, we propose a sociocultural-technical approach to address these challenges. We highlight five key areas for development: (1) building a global clinical repository to support LLMs training and testing, (2) designing ethical usage settings, (3) refining diagnostic categories, (4) integrating cultural considerations during development and deployment, and (5) promoting digital inclusivity to ensure equitable access. We emphasise the need for developing representative datasets, interpretable clinical decision support systems, and new roles such as digital navigators. Only through collaborative efforts across all stakeholders, unified by a sociocultural-technical framework, can we clinically deploy LLMs while ensuring equitable access and mitigating risks.

This editorial summary provides an overview of the 39th annual meeting of the International Society for Traumatic Stress Studies (ISTSS) held from November 1-4, 2023. The meeting, themed "Scalable Strategies to Address the Impact of Trauma Worldwide: Innovations and Implementation," encouraged presenters to focus on the scalability of traumatic stress research and practice. The articles presented at the meeting, which form this issue, cover a broad spectrum of topics, from basic biological mechanisms to innovative digital engagement to population-wide treatments. These articles emphasize eliminating biases in research design and psychiatric practice, promoting health equity, and addressing the unique challenges of traumatic stress. This summary underscores the importance of scalability in developing flexible, dynamic, and inclusive mental health care interventions.

Firefighters are at increased risk of developing posttraumatic stress disorder (PTSD) due to exposure to potentially traumatic events during their careers. However, little is known about the prevalence of PTSD among this population, particularly when taking moderating variables into account. Using Gaussian Graphical Models and Directed Acyclic Graphs, we conducted network analyses to examine the interactions between clusters of PTSD symptoms, perceived stress, hardiness, and experiential avoidance among 187 firefighters. The data and code are published with the paper. Experiential avoidance, as part of psychological inflexibility, was found to be the only variable that interacted with PTSD symptomatology. Strong positive associations were observed between experiential avoidance and the "negative mood and cognitions" subscale of the PTSD Checklist for DSM-5 (PCL-5). Through this association, other PTSD symptoms were activated, particularly avoidance and arousal. Our findings suggest that experiential avoidance and negative mood and cognition symptoms are particularly important in the expression of PTSD symptomatology in firefighters. In addition, experiential avoidance may be used as a coping strategy to reduce perceived stress during potentially traumatic events. Therefore, experiential avoidance may be a prime target for future interventions and training focused on flexible self-regulation strategies in this population.

The originally published version of this article contained mistake and the authors would like to correct it. The error concerns the name of one of the authors. In the original publication, the author"™s name was misspelled as "Belleart." However, the correct spelling should be "Bellaert." The authors would like to apologize for this oversight and would like to rectify the mistake through this Erratum. The original article has been corrected.

The Attentional Control Scale (ACS) is a widely used self-reported assessment of attentional control. Due to the importance of those executive processes in the phenomenology of mood-related disorders, the ACS has been translated in multiple languages. Our purpose was to explore psychometric properties of two versions of the French ACS. In study 1, 455 participants completed the original ACS, which yielded excellent fit to a two-dimensional model (CFI = 0.972). However, as one factor contained in majority reversed coded items, this raised question about its validity. A second sample (N = 452) therefore completed a modified version of the ACS without reverse-coded items, which also yielded excellent fit to a two-dimensional model (CFI = 0.970). Finally, network analyses explored the relations between the ACS and symptoms of depression, anxiety, and resilience. Our results support the use of the French version of the ACS with items coded in a straightforward manner.

Substance use disorders (SUD) are associated with increased risk of worse COVID-19 outcomes. Likewise, racial/ethnic minority patients experience greater risk of severe COVID-19 disease compared to white patients. Providers should understand the role of race and ethnicity as an effect modifier on COVID-19 severity among individuals with SUD. This retrospective cohort study assessed patient race/ethnicity as an effect modifier of the risk of severe COVID-19 disease among patients with histories of SUD and overdose. We used merged electronic health record data from 116,471 adult patients with a COVID-19 encounter between March 2020 and February 2021 across five healthcare systems in New York City. Exposures were patient histories of SUD and overdose. Outcomes were risk of COVID-19 hospitalization and subsequent COVID-19-related ventilation, acute kidney failure, sepsis, and mortality. Risk factors included patient age, sex, and race/ethnicity, as well as medical comorbidities associated with COVID-19 severity. We tested for interaction between SUD and patient race/ethnicity on COVID-19 outcomes. Findings showed that Non-Hispanic Black, Hispanic/Latino, and Asian/Pacific Islander patients experienced a higher prevalence of all adverse COVID-19 outcomes compared to non-Hispanic white patients. Past-year alcohol (OR 1.24 [1.01-1.53]) and opioid use disorders (OR 1.91 [1.46-2.49]), as well as overdose history (OR 4.45 [3.62-5.46]), were predictive of COVID-19 mortality, as well as other adverse COVID-19 outcomes. Among patients with SUD, significant differences in outcome risk were detected between patients of different race/ethnicity groups. Findings indicate that providers should consider multiple dimensions of vulnerability to adequately manage COVID-19 disease among populations with SUDs.

Pre-existing mental disorders are linked to COVID-19-related outcomes. However, the findings are inconsistent and a thorough analysis of a broader spectrum of outcomes such as COVID-19 infection severity, morbidity, and mortality is required. We investigated whether the presence of psychiatric diagnoses and/or the use of antidepressants influenced the severity of the outcome of COVID-19. This retrospective cohort study evaluated electronic health records from the INSIGHT Clinical Research Network in 116,498 individuals who were diagnosed with COVID-19 between March 1, 2020, and February 23, 2021. We examined hospitalization, intubation/mechanical ventilation, acute kidney failure, severe sepsis, and death as COVID-19-related outcomes. After using propensity score matching to control for demographics and medical comorbidities, we used contingency tables to assess whether patients with (1) a history of psychiatric disorders were at higher risk of more severe COVID-19-related outcomes and (2) if use of antidepressants decreased the risk of more severe COVID-19 infection. Pre-existing psychiatric disorders were associated with an increased risk for hospitalization, and subsequent outcomes such as acute kidney failure and severe sepsis, including an increased risk of death in patients with schizophrenia spectrum disorders or bipolar disorders. The use of antidepressants was associated with significantly reduced risk of sepsis (p = 0.033), death (p = 0.026). Psychiatric disorder diagnosis prior to a COVID-19-related healthcare encounter increased the risk of more severe COVID-19-related outcomes as well as subsequent health complications. However, there are indications that the use of antidepressants might decrease this risk. This may have significant implications for the treatment and prognosis of patients with COVID-19.