Scientific research is a high-impact experience for undergraduate students. While there are many benefits of involving students in research programs, mentored research models cannot accommodate all interested students due to limited time and bandwidth of faculty mentors. Additionally, these experiences often cannot be accessed by students from marginalized backgrounds, amplifying issues of inequity in the sciences. In response to these limitations, other models of student research engagement such as course-based undergraduate research experiences (CUREs) have been widely implemented. Although CUREs allow for greater scalability than one-on-one mentored research approaches, they fail to engage students in research activities in a sustained way that leads to higher persistence in STEM. Below, we first outline the benefits and problems associated with both mentored research and CURE models. We then propose Structured Team-based Inquiry in Labs (STIL)—an alternative strategy for researchers interested in providing undergrads with research opportunities. This hybrid approach bridges traditional mentored research and CURE-based opportunities to maximize the benefits of both approaches. To illustrate this model, we present two case studies in a microbial ecology and a clinical psychology research laboratory, showcasing the interdisciplinary implementation potential of the STIL model.

Although disgust proneness has been implicated in the development of obsessive-compulsive disorder (OCD), studies to date have failed to identify the core symptom of disgust proneness that may be most strongly associated with OCD and its treatment. Accordingly, we used network analysis to examine which symptoms are most central to disgust proneness and the extent to which such symptoms predicted OCD symptoms before and after treatment. Adolescent participants (N = 434) in a residential treatment program for severe OCD, anxiety, and mood disorders completed pre- and post-treatment measures of OCD, disgust proneness, and general mood symptoms. Network analysis was used to identify the most central symptoms (nodes) and associations between symptoms (edges) of disgust proneness. The results revealed that the most central indicator in the pre- and post-treatment network were symptoms of ‘animal-reminder disgust’ (i.e., seeing blood in meat at the grocery store) that remind humans of their own mortality and bodily vulnerability. However, the pre- and post-treatment networks did not significantly differ (i.e., no network invariance). Accounting for OCD diagnostic status, animal-reminder disgust symptoms that were central in the network accounted for significant variance in OCD and general mood symptoms at pre- and post-treatment. These findings suggest that aversion towards animal-reminder cues are central symptoms of disgust proneness that may have predictive validity. The implications of these findings for conceptualizing theoretical and treatment models of disgust proneness in OCD are discussed.

Set-theoretic configural psychometrics identifies minimally sufficient conditions (concurrent state combinations) for clinical targets. We tested whether intraindividual rules derived from early ecological momentary assessment (EMA; first 60%) consistently account for future suicidal ideation and self-injury (held out 40%), and whether engine performance profiles can be preregistered. Across discovery (N = 46) and replication (N = 54) samples of recently suicidal trans and nonbinary adults, we evaluated constructive and exhaustive engines across four targets and tested nine preregistered behavioral criteria. Person-specific rules replicated forward in time (median replication .65–1.00). The constructive engine achieved higher specificity, whereas the exhaustive engine yielded higher sensitivity, coverage, and discrimination. Up to eight of nine preregistered criteria were confirmed, with replication strongly graded by target base rates. Configural psychometrics recovers valid intraindividual rules for future observations. Its performance is predictable and highest for chronically, rather than episodically, symptomatic individuals.

Network analysis is a popular method researchers use to characterize the structure of psychopathology and inform personalized treatments. Typically, applied researchers, based on network theory, interpret symptoms with the highest strength centrality as most important to network structure and represent amenable treatment targets. This study examines the stability of strength centrality in idiographic networks in a sample of participants with eating disorders (N = 26, 90-day assessment, M = 356.00 observations per person) and a second sample of participants with social anxiety disorder (N = 42, 30-day assessment, M = 201.90 observations per person). We estimated idiographic networks using three different item-inclusion approaches and accounted for time using a "sliding window" method (e.g., Window 1 = data from Days 1-15, Window 2 = data from Days 2-16). Items included in networks were selected in three ways: default networks (six items with the highest means at Window 1), changing means networks (six items with the highest means at each respective Window), and random ensembles (random combinations of any six items across all sliding windows). In both samples, we found that the most central symptom in the default network was central in less than half of idiographic changing means networks (maximum = 29.41% of networks). Our results show that node strength centrality estimates are sensitive to item ensemble and temporal effects. We discuss implications concerning inferences assigned to strength centrality given the frequency at which strength centrality changes and future efforts developing network-informed personalized treatment. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Objective: Psychological treatment effects and response rates have largely plateaued over the past few decades. A potential answer to this problem is personalized treatment approaches, which match treatment to a client’s specific presenting concerns, which should increase its precision and efficacy. Examining predictors and moderators of treatment outcome (who is likely to benefit from treatment, and which treatment) is one way to guide such personalized decision making. The current study aimed to investigate the relationship between network-derived subgroups, treatment condition, and treatment response using data from two clinical trials on eating disorders (N = 80). This study is also a proof-of-concept for the clinical utility of network-based subgroups. Method: Subgroups were identified using Subgrouping Group Iterative Multiple Model Estimation (S-GIMME) and ANOVAs were used to compare changes in symptom severity and clinical impairment among subgroups. Results: We found three subgroups (n = 71; mean age = 34.4 [SD = 11.8], 87.3% cisgender women, 85.9% white, non-Hispanic), which were differentiated by how shame and guilt were related in the network. The subgroup with a contemporaneous pathway from guilt to shame showed the least improvement in clinical impairment from pre- to posttreatment (F(2, 64) = 5.92, p = .004). Conclusions: Overall, our findings suggest that network-based subgroups may have utility as prognostic indicators, at least in the context of eating disorders, though replication of present findings is warranted. Limitations included potentially unstable subgroups and use of mostly cisgender women and white samples.

Eating disorders (EDs) have been traditionally viewed as a disorder affecting cisgender, heterosexual women. Yet, the prevalence of EDs among queer and trans (QnT) individuals, coupled with the lack of interventions that attend to contextual factors related to sexual orientation and gender identity, underscore a critical health disparity issue requiring urgent attention. Here, we first review factors pertaining to QnT individuals’ minoritized sexual and gender identities that are important to consider in ED conceptualization for this population (e.g., minority stressors, identity-based body image standards). Next, we describe problematic assumptions present in existing ED assessment and propose more inclusive approaches. Lastly, we provide suggestions for practices that providers can implement within their treatment of EDs among QnT individuals.

Item selection is a critical decision in modeling psychological networks. The current preregistered two-study research used random selections of 1,000 symptom networks to examine which eating disorder (ED) and co-occurring symptoms are most central in longitudinal networks among individuals with EDs (N = 71, total observations = 6,060) and tested whether centrality changed based on which items were included in the network. Participants completed 2 weeks of ecological momentary assessment (five surveys/day). In Study 1, we obtained initial strength centrality values by estimating an a priori network using eight items with the highest means. We then estimated 1,000 networks and their centrality from a random selection of unique eight-item symptom combinations. We compared the strength centrality from the a priori network to the distribution of strength centrality estimates from the random-item networks. In Study 2, we repeated this procedure in an independent longitudinal dataset (N = 41, total observations = 4,575) to determine if our results generalized across samples. Shame, guilt, worry, and fear of losing control were consistently central across networks, regardless of items included in the network or sample. Results suggest that these symptoms may be important to the structure of ED psychopathology and have implications for how we understand the structure of ED psychopathology. Existing methods for item inclusion in psychological networks may distort the structure of ED symptom networks by either under- or overestimating strength centrality, or by omitting consistently central symptoms that are nontraditional ED symptoms. Future research should consider including these symptoms in models of ED psychopathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

OBJECTIVE:Dysregulated eating is common among youth and is associated with trait-level negative affect and emotion regulation difficulties. Despite the transient nature of affect, momentary associations among affect and eating behavior are unclear, which limits development of more impactful treatment tools, such as "just-in-time" intervention approaches (JITAI). The current study (N = 62) drew from two ecological momentary assessment (EMA) studies involving children and adolescents who endorsed loss of control (LOC) eating symptoms during a two-week assessment period. METHOD:Intensive time series network analysis tested concurrent and prospective relationships across six specific affective states (i.e., upset, guilty, scared, tired, excited, attentive) and four eating-related experiences (e.g., LOC, overeating, hunger, craving) in real time. Additionally, we repeated these models within demographic subgroups of the sample based on age, race, and sex. RESULTS:In the full-sample models, contemporaneously assessed guilt was associated with craving and LOC eating, and tiredness was associated with LOC eating. In the prospective analysis, tiredness was negatively predicted by LOC eating and positively predicted by overeating at the previous timepoint, and attentiveness positively predicted craving. Differences in affect-eating relationships were identified across teens and preteens as well as male and female participants. DISCUSSION:These results suggest that specific affective states are associated with dysregulated eating-related experiences in real time among youth, and associations may differ depending on demographic characteristics. Findings may be used to inform the development and tailoring of momentary interventions.

Sexual and gender minority (SGM) individuals show disproportionately high rates of mental distress relative to their cisgender, heterosexual peers resulting from minority stress, or unique identity-related stressors. The majority of research on minority stress and mental health in SGM individuals has focused on adults, a notable gap given that SGM youth face unique developmental factors that intersect with identity development and availability of support resources. SGM youth therefore represent a critical population for the mental health workforce to serve competently. Mental health providers risk significant harm to their SGM youth clients if they do not understand the mechanisms underlying mental health disparities in this population. This article will review treatment practices that carry the potential for harm with SGM youth, including harms that are more overt and attempt to change SGM identities (i.e., so-called “conversion therapies”), and others that are more covert, such as neglecting to consider SGM identity in conceptualization and treatment (e.g., eating disorders), pathologizing SGM identity and behaviors (e.g., personality disorders, social anxiety), and reinforcing stigma related to SGM identities (e.g., obsessive-compulsive disorder). Accordingly, this article reviews each of these potential harms in detail and provides alternative recommendations for affirming and justice-based treatment for SGM youth.

ObjectiveBody mass index (BMI) is the primary criterion differentiating anorexia nervosa (AN) and atypical anorexia nervosa despite prior literature indicating few differences between disorders. Machine learning (ML) classification provides us an efficient means of accurately distinguishing between two meaningful classes given any number of features. The aim of the present study was to determine if ML algorithms can accurately distinguish AN and atypical AN given an ensemble of features excluding BMI, and if not, if the inclusion of BMI enables ML to accurately classify between the two.MethodsUsing an aggregate sample from seven studies consisting of individuals with AN and atypical AN who completed baseline questionnaires (N = 448), we used logistic regression, decision tree, and random forest ML classification models each trained on two datasets, one containing demographic, eating disorder, and comorbid features without BMI, and one retaining all features and BMI.ResultsModel performance for all algorithms trained with BMI as a feature was deemed acceptable (mean accuracy = 74.98%, mean area under the receiving operating characteristics curve [AUC] = 74.75%), whereas model performance diminished without BMI (mean accuracy = 59.37%, mean AUC = 59.98%).DiscussionModel performance was acceptable, but not strong, if BMI was included as a feature; no other features meaningfully improved classification. When BMI was excluded, ML algorithms performed poorly at classifying cases of AN and atypical AN when considering other demographic and clinical characteristics. Results suggest a reconceptualization of atypical AN should be considered.Public SignificanceThere is a growing debate about the differences between anorexia nervosa and atypical anorexia nervosa as their diagnostic differentiation relies on BMI despite being similar otherwise. We aimed to see if machine learning could distinguish between the two disorders and found accurate classification only if BMI was used as a feature. This finding calls into question the need to differentiate between the two disorders.

This study uses time-intensive, item-level assessment to examine individual depressive and co-occurring symptom dynamics. Participants experiencing moderate-severe depression (N = 31) completed ecological momentary assessment (EMA) four times per day for 20 days (total observations = 2480). We estimated idiographic networks using MDD, anxiety, and ED items. ED items were most frequently included in individual networks relative to depression and anxiety items. We built ridge and logistic regression ensembles to explore how idiographic network centrality metrics performed at predicting between-subject depression outcomes (PHQ-9 change score and clinical deterioration, respectively) at 6-months follow-up. For predicting PHQ-9 change score, R2 ranged between 0.13 and 0.28. Models predicting clinical deterioration ranged from no better than chance to 80 % accuracy. This pilot study shows how co-occurring anxiety and ED symptoms may contribute to the maintenance of depressive symptoms. Future work should assess the predictive utility of psychological networks to develop understanding of how idiographic models may inform clinical decisions.

The relationship between negative emotions and avoidance is widely theorized as a bidirectional cycle implicated in a range of psychopathology. Historically, research on this cycle has examined one type of negative emotion: anxiety. Yet, a broader range of internal experiences may be implicated in the maintenance of unhealthy avoidance cycles in psychopathology. This study examines prospective relationships among anxiety, guilt, physical discomfort, and experiential avoidance during mealtimes for individuals with eating disorders (EDs). Participants (N = 108) completed ecological momentary assessments four times a day for 25 days. We computed multilevel models to examine between- and within-person effects of negative emotions and physical discomfort on experiential avoidance. When including guilt and anxiety in one model, guilt, but not anxiety, explained the significant variance in experiential avoidance at the next meal. Mealtime physical discomfort and experiential avoidance evidenced reciprocal prospective relationships. Future research should test whether interventions targeting experiential avoidance and physical discomfort at mealtimes disrupt guilt.

This exploratory study aimed to describe the lived experiences of queer women affected by eating and weight-related concerns. Qualitative data from young queer women (n = 105; Age = 23.6 ± 3.4 years) with eating and weight-related concerns in response to open-ended questions related to the influence of gender identity and body image on weight concern, behaviours, and perception were analysed using reflexive thematic analysis. Nine themes were created to describe participants' experiences: (1) compensation for other internalised stigma, (2) to suppress body parts that can be gendered or sexualised, (3) comparisons to romantic partners' bodies, (4) media representations, (5) queer signalling, (6) queerness as protective, (7) gender expression and dysphoria, (8) societal expectations of women's bodies, and (9) internalisation of body/beauty ideals. Seven sub-themes were created to represent beauty ideals for specific subcultural communities (e.g. femme, butch). Findings suggest that queer women attribute individual, interpersonal and social factors to weight concerns, behaviours and perceptions. Findings highlight how complex tensions between the beauty/body ideals experienced in cisheteronormative and queer spaces influence eating and weight concerns among queer women. Gender, sexual orientation and subcultural ideals intersect in important ways, and may be useful to consider when screening, treating and preventing eating and weight concerns among queer women.

Eating disorders are serious psychiatric illnesses associated with large amounts of suffering, high morbidity, and high mortality rates, signifying a clear need for rapid advancements in the underlying science. Relative to other fields of clinical psychological science, the eating disorder field is new. However, despite the fields' late beginnings, there is growing science in several important areas. The current paper discusses the current literature in three primary areas of importance: (a) diversity and inclusion, (b) systemic and social factors, and (c) treatment personalization. We discuss how these areas have huge potential to push both eating disorder and clinical psychological science in general forward, to improve our underlying understanding of psychological illness, and to enhance treatment access and effectiveness. We call for more research in these areas and end with our vision for the field for the next decade, including areas in need of significant future research.

Diet culture is a societal norm that ranks thin bodies as superior to other body types and has been associated with negative outcomes, such as eating disorders. Wellness has evolved into a term that is often used to promote diet culture messages. One possible way to combat diet culture is through single-session, digital mental health interventions (DMHIs), which allow for increased access to brief public health treatments. The framing of DMHIs is critical to ensure that the target population is reached. Participants (N = 397) were enrolled in a single-session DMHI, which was framed as either a Diet Culture Intervention (n = 201) or a Wellness Resource (n = 196). Baseline group differences in eating disorder pathology, body image, weight stigma concerns, fat acceptance, and demographic characteristics were analyzed. Across groups, participants reported moderately high eating disorder pathology, low-to-moderate levels of body dissatisfaction, moderate levels of fat acceptance, and either very low or very high weight stigma concerns. Participants in the Diet Culture Intervention group reported higher levels of fat acceptance than those in the Wellness Resource group (p < 0.001). No other framing group differences were identified, though post hoc analyses revealed differences based on recruitment source (i.e., social media versus undergraduate research portal). This study found that framing a DMHI as targeting diet culture or as a Wellness Resource can result in the successful recruitment of individuals at risk of disordered eating. Framing a DMHI as a Wellness Resource may increase recruitment of individuals with low levels of fat acceptance, which may be particularly important for dismantling diet culture, disordered eating, and weight stigma concerns. Future research should assess DMHI framing in other populations, such as men and adolescents.

Major Depressive Disorder (MDD) is a prevalent psychiatric disorder impacting 10-16% of Americans in their lifetime. Approximately 60% of individuals with MDD have comorbid anxiety disorders. Additionally, although scarce research has examined eating disorders (EDs) in depression, a bidirectional association exists between ED and MDD symptoms. The current pilot study (N = 31 individuals with moderate to severe depression) modeled networks of depressive, anxiety, and ED symptoms using intensive time-series data. This study also tested if temporal central symptoms predicted six-month clinical outcomes. The most central symptoms were guilt, self-dislike, lack of energy, and difficulty concentrating. Several anxiety and ED symptoms were also central, including physical anxiety, social anxiety, body dissatisfaction, and desire for thinness. The central symptom crying predicted six-month depression with a medium effect size. These findings suggest anxiety and ED symptoms may influence the day-to-day course of depression in some individuals with comorbid diagnoses, but predictors of symptoms across hours may differ from predictors across longer time scales (i.e., months). Time scale should be considered when conducting and interpreting research on MDD. Research, assessment, and treatment for MDD should continue to explore transdiagnostic approaches including anxiety and ED symptoms to optimize care for individuals with complex presentations.

ObjectiveThe COVID-19 pandemic resulted in a shift from traditional, in-person treatment to virtual treatment for eating disorders (EDs), with little knowledge about the relative efficacy of virtual formats. MethodIn the current study, we examined baseline symptomatology and treatment outcomes of young adults in our virtual partial hospitalization and intensive outpatient program (PHP/IOP) for EDs, implemented shortly after the onset of the COVID-19 pandemic. We investigated outcomes on body mass index, ED symptoms, anxiety, ED-related clinical impairment, and emotion regulation. ResultsWe found significant differences in ED symptomatology, ED-related clinical impairment, and difficulties with emotion regulation at admission between participants in the virtual and in-person versions of our PHP/IOP. Despite these differences, the results demonstrated that the degree of change from admission to discharge on these measures was comparable for both conditions. DiscussionThese findings suggest that PHPs and IOPs are relatively effective in a virtual format. Providing effective virtual options across various levels of care will improve access to specialized treatment for EDs.

Ecological momentary assessment (EMA) data have a broad base of application in the study of time trends and relations. In EMA studies, there are a number of design considerations which influence the analysis of the data. One general modeling framework is particularly well-suited for these analyses: state-space modeling. Here, we present the state-space modeling framework with recommendations for the considerations that go into modeling EMA data. These recommendations can account for the issues that come up in EMA data analysis such as idiographic versus nomothetic modeling, missing data, and stationary versus non-stationary data. In addition, we suggest R packages in order to implement these recommendations in practice. Overall, well-designed EMA studies offer opportunities for researchers to handle the momentary minutiae in their assessment of psychological phenomena.

•Personalizing psychological treatments means to customize treatment for individuals to enhance outcomes.•The application of precision methods to clinical psychology has led to data-driven psychological therapies.•Applying data-informed psychological therapies involves clinical, technical, statistical, and contextual aspects.