BACKGROUND:Although they constitute well-established risk factors for suicidal thoughts and behaviors (STBs), few studies have investigated how family factors such as family cohesion and family conflict influence the trajectory of treatment response among depressed and suicidal adolescents. In this study, we examined the association between baseline family factors and response to an intensive outpatient program (IOP) for STBs in adolescents. METHODS:Participants (n = 637) either reported their satisfaction with family cohesion or family conflict levels at baseline and provided weekly self-reports of depression and STBs throughout IOP treatment. We calculated multi-level regression models to test for the interaction effects of treatment-day × family-factor-levels and explored further demographic and clinical moderators. RESULTS:Higher levels of family cohesion correlated with more reduction of STBs over time (β = -0.11). Moreover, the effects of family cohesion and family conflict on treatment were moderated by patient age, ethnic minority status, and symptom severity. DISCUSSION:STBs and depression improved with IOP, with family cohesion partially moderating treatment response. Differences due to family factors were more prominent in older and ethnically minoritized adolescents, and those with more severe symptoms. Future studies should elucidate how family factors co-vary with changes in STBs during treatment to better understand these effects.

OBJECTIVE:The nature of associations between disinhibition, anankastia, and conscientiousness as overlapping domains of control is not well understood. This study aimed to parse the shared and unique components of trait disinhibition and anankastia and examine their associations with trait conscientiousness and relevant outcomes. METHOD:We used confirmatory bifactor analysis across two preregistered samples of undergraduates and community adults (total N = 1068). RESULTS:Across both samples, disinhibition and anankastia were represented by specific latent factors, but a coherent general factor capturing shared variance across the two domains did not clearly emerge. The disinhibition-specific factor had small negative associations with the anankastia-specific factor and large negative associations with latent conscientiousness. In contrast, the anankastia-specific factor was moderately and positively related to latent conscientiousness. Disinhibition showed the most consistent and robust associations with poorer functioning and behavioral addiction outcomes, whereas associations for anankastia were fewer and more modest. CONCLUSIONS:These findings suggest that disinhibition and anankastia are largely distinct trait dimensions. Disinhibition and conscientiousness appear to reflect closely related but opposing traits, whereas anankastia and conscientiousness are positively related but separable. This study has implications for the broader structure of personality pathology and the polarity of maladaptive traits.

Etiological models of alcohol and cannabis use disorders hypothesize that people are more likely to use substances when experiencing heightened negative affect, yet recent EMA studies found no evidence for this daily association. To provide a robust understanding of whether and when affect regulation is supported in EMA, we tested within-person associations between affect and substance use across hundreds of statistical models in a diverse sample of young adults (N = 496) recruited from both college and community sources, aged 18-22 years (55.8% assigned female sex at birth, 44.2% assigned male sex at birth; 47.2% cisgender female, 43.8% cisgender male, 12.9% nonbinary/genderqueer/gender non-conforming, 4.0% transgender; 69.6% non-Hispanic White, 26.2% Asian, 6.7% African American, 8.5% Hispanic/Latino). Using specification curve analyses, we examined how different affect operationalizations, time scales, and moderators influenced these associations. For alcohol use, higher negative affect predicted decreased likelihood of drinking (median OR = 0.95, p < .001), with 20.6% of specifications reaching significance. This counter-intuitive pattern was strongest for sadness and when examining maximum daily negative affect. Surprisingly, and contrary to theoretical predictions, this negative association was slightly more pronounced among those with higher coping motives and at lower levels of AUD symptoms. Positive affect showed a complex pattern, with high-arousal states like joviality strongly predicting increased drinking likelihood, while low-arousal states showed weaker associations. Neither affect type consistently predicted drinking quantity. For cannabis use, neither positive nor negative affect predicted use likelihood or quantity across specifications. These associations remained consistent regardless of substance use disorder severity or social context. Our findings challenge core assumptions of affect regulation models and suggest that, at least in young adults, the affect-substance use relationship is more nuanced than previously theorized, with implications for refining etiological models.

Adolescence is a period of heightened stress vulnerability, yet the microtemporal dynamics of affective stress responses remain poorly understood. Using an ecological momentary assessment design with four 15-minute microburst-assessments within the first post-stressor hour, we tracked negative (NA) and positive affect (PA) in 288 adolescents (ages 12–21) over 14 days. Piecewise multilevel growth curve models, compared across three affective reference points (i.e., baselines), revealed sharp reactivity followed by rapid initial stabilization succeeded by decelerated recovery. Stressor intensity amplified both NA and PA reactivity and decelerated later NA recovery. Interpersonal stressors elicited stronger NA reactivity, whereas social company buffered PA decline. Adolescents with elevated internalizing symptoms showed profoundly reduced PA levels - exceeding corresponding NA elevations - alongside initially faster but subsequently decelerated PA recovery. Greater stress exposure robustly dampened affective reactivity, with effects on recovery varying by baseline specification. Results underscore the importance of temporally aligning theoretical constructs with methodological decisions.

Closely tracking changes in depression is important for clinical research and practice. However, intensive longitudinal depression assessment relies on self-report surveys that have major limitations. Scoring depression from natural language with large language models (LLMs) could resolve these shortcomings. This study tested the validity of depression rated by LLMs from daily video diaries. We studied 108 participants selected for mental health treatment status (45% in treatment). Participants completed a clinical interview and 14-21 days of depression surveys and video diaries. Daily depression scores were estimated by LLMs from diary transcripts. LLM ratings converged with self-report depression within-person (r=.45;95% CrI[.40, .49]) and when averaged across days for an overall depression score (r=.61;95% CrI[.43,.75]). Averaged LLM ratings correlated with depression symptom severity ascertained by clinical interview (r=.53;95% CrI[.35, .68]). LLM-rated depression and self-report had similar profiles of associations with daily functioning variables (profile r=.98 within-person; r=.94 between-person). Multivariable regression results showed LLM ratings predicted daily functioning variables (partial βs=|.12|-|.28|) and interview-rated depression (partial β=.33;95% CrI[.07, .58]) over and above self-reports, suggesting LLMs capture clinically-relevant information that self-reports miss. Findings establish a strong foundation for a depression assessment approach that addresses shortcomings of self-reports while satisfying the unique demands of intensive longitudinal monitoring.

Passive smartphone sensing offers a scalable, low-burden approach to continuous mental health monitoring, yet most studies which use passive sensing features to predict momentary psychological states rely on small, clinically homogeneous samples, limiting generalizability and clinical utility. This study compares methods for personalized prediction of momentary affect, stress, and fatigue using passive sensing in a large, clinically diverse sample of 308 adults (66% with a psychiatric diagnosis). Participants completed 15 days of ecological momentary assessment (23,560 total observations) with passive sensing of GPS, accelerometer, phone call, screen-on-time, and sleep data. We predicted five continuous outcomes: positive affect, negative affect, stress, energy, and a depression composite. We explored methods for balancing group-level and personalized prediction, including mixed effects regression tree (MERT) boosting, a method that blends mixed effects and machine learning approaches. We compared this to group-level pooled models, fully personalized models, and linear mixed models. A key finding was that the use of cluster-mean centering (CMC) as a preprocessing step dramatically improved pooled machine learning model performance, enabling a simple XGBoost model to match or exceed fully personalized and MERT boosting approaches. Best models compared favorably to past work predicting continuous momentary affect, achieving overall R-squared of 0.38-0.49 and average idiographic R-squared of 0.16-0.26 calculated on held out test data, with energy and the depression composite emerging as the most predictable outcomes. These results carry direct clinical implications: scalable group-level models can rival personalized models when data are appropriately preprocessed, lowering barriers to real-world deployment of passive sensing–based monitoring. The high predictability of energy and the depressive composite highlights viable targets for just-in-time adaptive interventions. This work advances digital phenotyping by demonstrating clinically relevant affect prediction at scale and identifying CMC as a critical tool for scalable personalization of mental health monitoring.

Contemporary personality assessment relies heavily on psychometric scales, which offer efficiency but risk oversimplifying the rich and contextual nature of personality. Recognizing these limitations, this study explores the use of commercially available generative large language models (LLMs), such as ChatGPT, Claude, etc., to assess personality traits from open-ended qualitative narratives. Across two distinct samples and methodologies (spontaneous streams of thought and daily video diaries) we used generative LLMs to score Big-Five personality traits, achieving convergence with self-report measures comparable to or exceeding established benchmarks (e.g., self-other agreement, ecological momentary assessment, bespoke machine-learning models). LLM-generated trait scores also demonstrated predictive validity regarding daily behaviors and mental health outcomes. This LLM-based approach achieved quantitative rigor based on qualitative data and is easily accessible without specialized training. Importantly, our findings also reaffirm the ubiquity of personality expression, in that it is carried in the stream our thoughts and is woven into the fabric of our daily lives. These results encourage broader adoption of generative LLMs for psychological assessment, and—given the new generation of tools—stress the value of idiographic narratives as reliable sources of psychological insight.

Clinical psychology, like other disciplines, is facing a replication/credibility crisis that undermines the evidentiary basis of our science. The Open Science Movement (OSM) offers solutions through practices such as preregistration, Registered Reports, and sharing of data, materials, and code. Clinical psychology has been slow to adopt these reforms, leaving trainees underprepared for emerging norms. This article reviews the factors that contributed to the crisis, outlines the necessary role of transparency in distinguishing rigorous from flawed research, and documents the limited uptake of open science practices in clinical journals and graduate training. We argue that transparency is an ethical as well as methodological imperative and propose a competency-based model for embedding open science principles into doctoral education. We conclude by calling on programs, journals, and accrediting bodies to make transparency a core requirement, essential for restoring credibility and advancing a cumulative, trustworthy clinical science.

The Hierarchical Taxonomy of Psychopathology (HiTOP) is a dimensional nosological system that addresses key limitations with categorical frameworks, including heterogeneity, boundary, and comorbidity issues. The HiTOP consortium recently developed a new self-report instrument, the HiTOP-Self-Report Measure (HiTOP-SR), designed to operationalize the HiTOP model for use in research and clinical practice. In a set of preregistered analyses with a sample of clinical/community participants (75% female, 81% White), we explored the hierarchical structure of the HiTOP-SR scales using exploratory factor analysis (n = 637) and examined their associations with behaviors and experiences assessed in daily life (n = 531), such as affect, stress, impulsivity, energy, sleep quality, and social interactions. Findings indicate a nine-factor model, closely aligned with the HiTOP's current structure, best represented the measure. The hierarchical structure of the HiTOP-SR generally converges with the HiTOP model, with several key departures, particularly for historically understudied constructs. Furthermore, the HiTOP-SR facet scales and domains associated with individual differences in daily behavior and experiences as anticipated, highlighting the construct validity and the potential clinical utility of this new measure. Our results have implications not only for the structure, validity, and clinical utility of the HiTOP-SR but also raise broader questions about the underlying nature of psychopathology as represented by the HiTOP. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Background: Accurate psychiatric assessment requires understanding a person’s unique experience within their psychosocial context. Clinical interviews have been the gold standard for assessment as the only methods capable of this complex task, but they are time and resource intensive. Consequently, psychiatric assessment typically relies on patient report surveys that are decontextualized and narrow in scope. This comprehensiveness-scalability tradeoff is a major bottleneck in studying and treating psychopathology. We propose using large language models (LLMs) to score psychopathology from brief personal narratives as a low-burden, context-sensitive solution. Methods: Participants (N=108) completed brief (~1 minute), freeform audio diaries daily for two weeks. We used six LLMs to score wide-ranging psychopathology (Internalizing, Detachment, Disinhibition, Antagonism, and Anankastia) from the diary transcripts. Leveraging an array of self-report and clinical interview measures, we tested the convergent, discriminant, concurrent, and clinical validity of LLM ratings for both between-person differences and within-person fluctuations in psychopathology. Results: Supporting convergent and discriminant validity, LLM ratings correlated most strongly with corresponding self-report domains at both the between (average convergent r = .42) and within-person (r = .28) levels. LLM and self-report ratings had similar patterns of associations with external variables, except for Anankastia and Antagonism. Further, every LLM-rated domain related to psychopathology ascertained by clinical interview. Conclusions: Across multiple forms of validity, we showed that LLMs can assess most major forms of psychopathology from mere minutes of audio. These results support scoring open-ended narratives with LLMs as a scalable, portable method to translate idiographic diagnostic data into standardized psychiatric assessments.

Previous studies have predominantly viewed affective variability as detrimental to well-being, suggesting an unstable emotional state. However, research on early warning signs of affective disorders suggests that affective variability may also be adaptive, particularly when individuals' affective well-being is low. Here, we sought to test that greater affective variability would predict increased affective well-being over time (Hypothesis 1), or that better affective well-being would lead to lower affective variability over time (Hypothesis 2), and that the first relationship would be stronger for individuals with low prior levels of affective well-being (Hypothesis 3) and weaker for individuals high in neuroticism (Hypothesis 4). We tested this set of hypotheses by reanalyzing 14 ambulatory assessment data sets (N = 2,374 participants with 25,478 observations at the day level). Our integrative data analysis revealed that greater affective variability at time t₁ was significantly associated with better subsequent affective well-being at time t₂ at the day and year level. In addition, this association was significantly moderated by initial levels of affective well-being and by neuroticism, although the evidence for the latter was limited. These findings highlight the importance of distinguishing between within-person processes and between-person differences: Experiencing greater affective variability relative to others may indicate a lower level of overall affective well-being. At the same time, experiencing greater affective variability when feeling lower than usual may signal the potential for improvement in one's affective experience. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

The Hierarchical Taxonomy of Psychopathology (HiTOP) emerged to address critical shortcomings inherent to traditional psychiatric classification systems such as the Diagnostic and Statistical Manual of Mental Disorders and International Classification of Diseases, notably their categorical structure, high comorbidity across categories, and within-diagnosis heterogeneity. HiTOP adopts an empirically derived, dimensional, and hierarchical approach, organizing psychopathological phenomena based on their patterns of observed co-variation. This paper explores essential conceptual and philosophical considerations around HiTOP, examining its theoretical assumptions about dimensionality and hierarchy, the nature and interpretation of latent variables, the notion of psychopathology, considerations around validity, and the role of epistemic and non-epistemic values in shaping scientific objectivity. HiTOP is a descriptive model based on quantitative evidence (such as taxometric and factor-analytic approaches), but it is also a nosological project that exists within a particular sociocultural and historical context. As an illustration of the role of values, the applicability of HiTOP to marginalized minority populations is discussed, highlighting ongoing efforts toward ensuring inclusivity and representational equity. By addressing these conceptual foundations, this paper lays groundwork essential for future philosophical inquiry, empirical research, and practical applications of the HiTOP framework.

The nature of associations between disinhibition, anankastia, and conscientiousness as overlapping domains of control is not well understood. Using confirmatory bifactor analysis across two preregistered samples of undergraduates and community adults (total N = 1,068), this study parsed the shared and unique components of trait disinhibition and anankastia, and examined their associations with trait conscientiousness and relevant outcomes. Across both samples, disinhibition and anankastia were represented by specific latent factors, but a coherent general factor capturing shared variance across the two domains did not clearly emerge. The disinhibition-specific factor was modestly and negatively related to the anankastia-specific factor and strongly and negatively related to latent conscientiousness. In contrast, the anankastia-specific factor was moderately and positively related to latent conscientiousness, suggesting modest overlap with adaptive self-regulation. In terms of criterion validity, disinhibition showed the most consistent and robust associations with poorer functioning and behavioral addiction outcomes, whereas associations for anankastia were fewer and more modest. Together, these findings suggest that disinhibition and anankastia are largely distinct trait dimensions. Furthermore, disinhibition and conscientiousness appear to reflect closely related but opposing traits, whereas anankastia and conscientiousness are positively related but separable, with implications for personality pathology and over- and under-control tendencies.

Sexual and gender minority (SGM) individuals experience higher rates of depression compared to their peers. However, little is known about the day-to-day processes that link SGM experiences to depression. In responses from SGM young adults (N=252), who predominantly identified as bi+(sexual/romantic interest in multiple genders)cisgender women and nonbinary individuals assigned female at birth, we used multilevel structural equation modeling to investigate whether affective reactivity to SGM concealment and outness over an8-day ambulatory assessment period (4342 observations) was associated with depression symptoms, and whether there was an indirect effect of momentary identity functioning. Concealment and outness were differentially associated with self-concept clarity and SGM-identity positivity.We did not find a direct link between depression and affective reactivity, though person-centered affect linked depression and affective reactivity to concealment. This study indicates that momentary identity processes play an important role in the daily life experiences of visibility for bi+individuals.

Abstract In this chapter, psychopathology is conceptualized through the lens of Contemporary Integrative Interpersonal Theory (CIIT), a model of how a highly social species engages in its social world, how that can go wrong, and what can be done about it. To articulate cause and effect models of psychopathology, CIIT combines empirically validated structural models with temporally dynamic momentary and developmental social-cognitive, affective, and behavioral processes across levels of analysis to articulate adaptive and maladaptive patterns of functioning. The chapter first reviews CIIT’s major assumptions and provides CIIT’s definition of psychopathology in terms of distortion and dysregulation in interpersonal situations. It then discusses interpersonal case conceptualization, an approach that can be applied to dimensions of psychopathology, diagnostic categories of psychopathology, individual symptoms, general problems in living, and other clinically relevant constructs. Regardless of the nosological model, interpersonal case conceptualization can include interpersonal antecedents of psychopathology, interpersonal consequences of psychopathology, interpersonal pathoplasticity of psychopathology, and interpersonal disorders themselves. Finally, the role of interpersonal dynamics in dimensional models of psychopathology is discussed.

Impulsivity is a dynamic phenomenon shaped by situational context, yet it has historically been studied using static, trait-based measures. Moreover, impulsivity has been predominantly conceptualized as rash action, despite theoretical work suggesting that it may also be expressed as rash inaction, or the failure to act despite negative consequences. Using intensive ecological momentary assessment, the present study mapped the topography of daily impulsivity in a clinically enriched sample of 540 adults. Participants reported momentary impulsive behaviors and contextual information up to eight times per day over 15 days. Higher trait impulsivity was associated with greater within-person variability and instability in both impulsive action and inaction, and momentary impulsivity showed temporal dependency. Contextual factors differentially shaped impulsivity: action was more likely and intense later in the day, in public, and in social contexts, whereas inaction was more likely in the morning, at home, and during less structured activities.

Depression, anxiety, and other internalizing disorders impose a staggering burden on public health. Alterations in emotional experience are central to theory, but prospective-longitudinal studies of real-world affect dynamics are scarce. Leveraging a hierarchical-dimensional approach, we examined associations between momentary emotional experience and broadband and narrow internalizing symptoms in a risk-enriched sample of 234 emerging adults followed for 2.5 years. Bayesian models demonstrated that low tonic levels of positive affect (PA) were uniquely associated with worsening Well-being symptoms, a core feature of depression, social anxiety, and trauma disorders. Baseline differences in tonic negative affect were associated with the severity of future anxious-arousal symptoms, but not with longitudinal change. Variation in reactive affect and exposure to everyday positive and negative events were unrelated to symptom trajectories. These observations highlight the centrality of tonic PA to the development of a key transdiagnostic symptom and set the stage for developing more effective intervention strategies.

The Hierarchical Taxonomy of Psychopathology (HiTOP) is a dimensional nosological system that addresses key limitations with categorical frameworks, including heterogeneity, boundary, and comorbidity issues. The HiTOP consortium recently developed a new self-report instrument, the HiTOP-Self-Report Measure (HiTOP-SR), designed to operationalize the HiTOP model for use in research and clinical practice. In a set of preregistered analyses with a sample of clinical/community participants (75% female, 81% white), we explored the hierarchical structure of the HiTOP-SR scales using exploratory factor analysis (n = 637) and examined their associations with behaviors and experiences assessed in daily life (n = 531), such as affect, stress, impulsivity, energy, sleep quality, and social interactions. Findings indicate a nine-factor model, closely aligned with the HiTOP’s current structure, best represented the measure. The hierarchical structure of the HiTOP-SR generally converges with the HiTOP model, with several key departures, particularly for historically understudied constructs. Furthermore, the HiTOP-SR facet scales and domains associated with individual differences in daily behavior and experiences as anticipated, highlighting the construct validity and the potential clinical utility of this new measure. Our results have implications not only for the structure, validity, and clinical utility of the HiTOP-SR but also raise broader questions about the underlying nature of psychopathology as represented by the HiTOP.

Objective: Cancer survivors, defined as those living with or beyond a cancer diagnosis, experience more than double the prevalence of psychopathology when compared to the general population. Categorical diagnostic systems, such as the Diagnostic and Statistical Manual of Mental Disorders (DSM), remain dominant in psycho-oncology despite concerns about reliability, validity, and clinical utility for this population. Dimensional frameworks, such as the Hierarchical Taxonomy of Psychopathology (HiTOP), offer a more precise alternative; however, they have not yet been widely applied in cancer survivors. Accordingly, the objective of this research was to examine the applicability of HiTOP to cancer survivors. Methods: Data from 1,389 participants in 28 countries (n=728 cancer survivors; n=661 community/psychiatric) were collected using the 405-item HiTOP-SR, alongside demographic, clinical, and cancer-specific measures. The HiTOP-SR normative sample (n=780) was also used. Analyses included parametric, non-parametric, and factor analytic approaches. Results: All HiTOP-SR scales demonstrated strong homogeneity and reliability in cancer survivors. Cancer survivors showed significant elevations across Internalising and Somatoform spectra, with current cancer associated with additional elevations in domains of Thought Disorder and components of Disinhibited and Antagonistic psychopathology. An 11-factor model was developed and supported for both cancer and community/psychiatric samples, though the magnitudes of the factor loadings sometimes varied between samples. External validity was strong with theoretically aligned associations. Conclusion: The HiTOP-SR appears reliable within cancer survivors and provides utility in quantifying a broad array of psychopathology experienced. The results highlight the potential applicability and utility of HiTOP to improve cancer research and clinical practice in psycho-oncology.