The idea that racial prejudice contributes to discrimination not only deliberately but also in a more automatic fashion has been one of the most prominent topics in social psychological research in the past 30 years. Much of the evidence for theories of automatic prejudice stems from the use of indirect measures of implicit attitudes, yet meta-analyses give differing estimates regarding the predictive validity of such measures. The present adversarial collaboration provides a test of the relationships between prominent measures of implicit racial attitudes and discriminatory behavior using a set of established lab-based paradigms among a sample of White Americans (N = 2,114). Using structural equation models that can account for measurement error, frequentist and Bayesian multiverse analyses confirmed that White Americans' performance on indirect measures correlate modestly with these behavioral outcomes, and explain unique variance (∼2.5%) beyond direct, self-report measures of racial attitudes. At the same time, self-report measures exhibited greater predictive and incremental validity than indirect measures (explaining ∼45% of the variance) despite behavioral measures of discrimination displaying weak internal reliability. Results provided some support for greater predictive and incremental validity for indirect measures among participants scoring relatively low on measures of executive function and motivation to control prejudice. These results lend themselves to both relatively optimistic and pessimistic interpretations concerning scientific and practical significance. All collaborators agree that the best path forward is collaborative and focused on the generalizability of implicit racial attitudes to high-accountability organizational settings. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Anhedonia, a cardinal feature of depressive disorders, is classically defined as an inability to experience pleasure, but modern definitions also encompass deficits in anticipatory pleasure and motivation. Validated scales and behavioural tasks have evolved alongside these conceptual shifts, and when integrated with computational modelling, may help reveal mechanisms underlying anhedonia. Reinforcement learning (RL) is the dominant computational framework for studying anhedonia, but fails to fully capture anticipation and motivation. We reviewed the operationalization and measurement of anhedonia from a historical perspective, and conducted a scoping review and critical appraisal of 37 past studies that included computational models of anhedonia using a structured appraisal guide to assess face, construct and predictive validity. We focussed on generative models (i.e., models that can simulate behavioural data) that were paired with measures of anhedonia in both clinical and non-clinical samples. Model types include RL models, RL integrated with functional magnetic resonance imaging, RL integrated with electroencephalography, and models of decision making, effort expenditure, selective attention, and self-referential/memory processes. Our review suggests that anhedonia-related deficits span not only reward processing, but also executive and sensory processing. Models generally demonstrated face validity, lacked predictive validity, and showed construct validity for cognitive-behavioural, but not neurobiological, domains. We propose an integrative, systems neuroscience-inspired approach, which aligns with multidimensional definitions of anhedonia.

Development and preliminary validation of the Pressure To Lose Pregnancy-related Weight scale (PLPW) in U.S. military active-duty Service women.

The psychological impact of honor cultures and shame societies on the general population has not been examined through a diagnostic lens. Atimiaphobia is a newly recognized psychological condition characterized by an intense fear of losing honor or being labeled shameless, deeply rooted in honor cultures and shame societies. To assess this construct, the Atimiaphobia Scale (AtiPhoS) was developed and rigorously validated. The study was conducted in a series of four phases involving 1232 participants (Mage = 27 years; women = 48.9%). The validation of the AtiPhoS involved exploratory and confirmatory factor analyses along with convergent and predictive validity. The AtiPhoS, comprising 15 items (English) and four subscales (fear of being labeled shameless, fear of violating social norms, fear of public judgement, fear of losing self-respect and honor) demonstrated excellent reliability (α = 0.824; ICC = 0.989). The model fit indices, such as CFI (0.933), TLI (0.916), RMSEA (0.065), and SRMSR (0.044), showed strong validity. Convergent validity was demonstrated by the scale's significantly positive correlation with the Experience of Shame Scale (r = 0.377) and the anxiety sub-scale of the Depression, Anxiety, and Stress Scale (r = 0.262). The predictive validity of the AtiPhoS was established through its inverse predictive values for social intelligence (β = -0.229). A significant positive correlation was found between atimiaphobia and age. Women and married individuals exhibited significantly higher levels of atimiaphobia compared with men and unmarried individuals, respectively. The study provides compelling evidence that atimiaphobia is a distinct and measurable phenomenon, contributing to the broader understanding of cultural stressors related to honor and shame.

Bayesian model updating and model validation for fatigue life prediction of additively manufactured aluminum alloys

Activating NK cell immunity against prostate cancer with Hedysarum polybotrys Hand.-Mazz.: A multi-omics integration of computational prediction and experimental validation on PI3K/AKT and PD-1/PD-L1 axes.

Humans possess an evolved followership psychology that enables them to identify and endorse different types of leaders depending on situational demands. But what fundamental needs guide these follower endorsements? Across a preliminary study and five validation studies (N = 3,514), we developed and validated the Fundamental Follower Needs Inventory (FFNI)-a psychometrically robust measure that identifies six core follower needs: protection, affiliation, status, guidance (including vision and expertise), and fairness. In Studies 1 and 2, we conducted content validation, tested reliability, and confirmed the factor structure of the FFNI across three domains (general, political, and workplace), three countries (the United States, the United Kingdom, and China), and multiple time points. Study 3 demonstrated FFNI's convergent and discriminant validity. Studies 4 and 5 explored the nomological network, examining its antecedents, consequences, and both predictive and incremental validity. The FFNI provides a novel tool for researchers to investigate how follower needs vary across contexts and cultures and how these needs shape leader endorsements and perceptions of leadership effectiveness. Practically, the FFNI offers leaders a framework to better understand and respond to the psychological needs of those they lead. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Diverse thymol analogs from Eupatorium fortunei: Their anti-inflammatory activity and mechanism.

Neural network prediction and theoretical validation of nonlinear optical rectification in asymmetric double semi-V-shaped quantum wells Ge/Si0.15Ge0.85 under electric field

Risk and safety of children and youth in child protection systems: A systematic review of risk assessment instruments.

Applying the health belief model to understand the usage and experience of self-management techniques in knee osteoarthritis: A mixed-methods observational study.

Best-practice item writing and blueprint alignment are recommended for medical school assessments, yet few studies examine whether exam redesign improves both psychometric quality and external validity of preclinical block exams in the Step 1 pass/fail era. We therefore examined how consolidating two moderate-reliability gastroenterology (GI) examinations into a single vignette-based exam affected reliability and its relationship with the NBME Comprehensive Basic Science Examination (CBSE). We conducted a retrospective analysis of six cohorts (2019-2025; N = 867) who completed the GI examination(s) and the CBSE. Item statistics and KR-20 reliability coefficients were obtained from the examination platform. Associations between exam average and CBSE total and GI subsection scores were evaluated using Spearman correlations (ρ), and cohort trends were explored using Welch-corrected ANOVA. Mean exam average declined modestly (91.6-86.1), whereas CBSE means were stable (≈57-67). Exam reliability rose steadily (KR-20 = 0.51-0.75), with the largest gain after blueprint revision (0.55-0.69). Across all students, exam average correlated moderately with CBSE total (ρ = 0.44, p < 0.001) and GI subsection (ρ = 0.36, p < 0.001). Cohort-specific correlations strengthened over time, increasing from ρ = 0.36 in 2019-2020 to ρ = 0.57 in 2024-2025; cohorts with KR-20 ≥ 0.65 demonstrated stronger predictive validity than earlier cohorts. A clinician-led, vignette-rich, blueprint-driven redesign improved exam reliability and strengthened alignment with national benchmarks despite reduced testing frequency, supporting clinically integrated assessments as a strategy to enhance step-readiness without increasing assessment burden.

The clinician rated suicide crisis syndrome checklist (SCS-C): Structure, reliability, and concurrent validity among adult psychiatric inpatients.

The monitoring of one's own learning progress is a key process in models of self-regulated learning and a key predictor of self-regulated learning and academic success. Judgments of learning (JOLs) are an established measure for assessing people's monitoring of learning and have been found to predict learners' subsequent performance as well as effort regulation. However, most studies have been conducted in laboratory settings, involving relatively artificial learning materials and low-stakes tests. We evaluate the predictive validity of JOLs for learning performance and effort regulation in an ecologically valid learning environment by requesting aggregate JOLs in an intelligent tutoring system. 90 German university students used an intelligent tutoring system that provided practice exercises for self-regulated preparation for a statistics exam over the course of a semester. Aggregate JOLs for each chapter of the statistics course were assessed once per week (279 assessments in total). Dependent variables were learning performance as well as absolute and relative learning effort for each chapter, derived from the intelligent tutoring system's log files. JOLs significantly predicted learning performance ( β = 0.20, p < .001) and effort regulation ( β absolute = −0.12, p < .001, β relative = −0.07, p = .002). The present research demonstrates that JOLs have predictive power in real-world learning. It thus bridges the gap between experimental cognitive research and applied educational research on metamemory and self-regulation. • Judgments of learning (JOLs) are predictions of one's own future performance. • Little is known about JOL accuracy in ecologically valid learning environments. • We examined JOL accuracy in an intelligent tutoring system used for exam preparation. • JOLs predicted effort regulation and performance during exam preparation.

Beyond prediction: Advancing risk assessment and addressing the evidence deficit in the statutory child maltreatment context.

Cognitive screening is a vital part of geriatric emergency department (ED) care. Performance-based functional cognitive measures may better identify impairments and predict post-discharge outcomes in daily activities. Currently, there is no evidence base informing the selection of suitable functional cognition instruments for emergency care. We conducted a systematic review to find psychometrically sound clinically useful instruments for assessing functional cognition in older adults in the ED. Six databases were searched to identify performance-based instruments measuring functional cognition and related psychometric studies, using COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN). Studies with a mean age under 65 were excluded. Clinical utility in the ED was evaluated using a newly updated, author-designed, criterion-based consensus matrix. Results were synthesised into a best-evidence summary. We identified 28 instruments and 44 adaptations across 138 studies assessing eight of the nine measurement properties. Construct validity (discriminative and convergent) was most frequently evaluated (87 %). Three instruments met all eight clinical utility criteria; however, none have been tested in ED: the Texas Functional Living Scale (moderate-high sufficient construct validity); the Menu Task (moderate-high sufficient construct validity, low-very low sufficient internal consistency and reliability), and the Pillbox Test (moderate sufficient predictive, high sufficient discriminative, moderate inconsistent convergent validity). Overall, 61 % of psychometric studies were rated as very good methodological quality. Our study is the first to identify existing instruments with established psychometric evidence that also meet clinical utility criteria for use with older adults in the ED. Robust studies are urgently needed to evaluate predictive validity for post-discharge outcomes.

Objective Pupillometry and Dexterity Assessment in Surgical Trainees: A Pilot Study.

Background: Understanding the mental health challenges of refugee adolescents is critical for informing targeted educational policies and interventions. Yet few studies identify which school-based factors most strongly relate to their resilience and well-being. This study draws on a multi-informant dataset from 170 refugee students attending school in Greece, applying Bayesian variable selection to identify key predictors of mental health outcomes. Methods: We collected student ( n = 170 ), teacher ( n = 165 ) and parent/guardian ( n = 124 ) data about each student (Strengths and Difficulties Questionnaire, SDQ; Child and Youth Resilience Measure-Revised, CYRM-R); sense of belonging, school attendance. We ran two models, one based on student self-reports, the other based on teacher, parent, and guardian data. Results: The self-reported sense of belonging was the most consistent protective factor of SDQ and CYRM-R outcomes, while bullying, discrimination, and disrupted schooling were associated with greater vulnerability. In triangulated analysis, student self-reports contributed the largest share of predictive information, followed by adult reports Discussion: This study highlights the importance of centering youth perspectives in mental health assessment and intervention and the critical role of inclusive school environments and educational continuity in supporting refugee integration. The Bayesian approach offers a robust framework for guiding evidence-based interventions for supporting the integration of refugee students in host-country educational settings. • We analyzed multi-informant data on refugee adolescents’ mental health difficulties and resilience. • Sense of belonging emerged as the strongest predictor of school-based mental health and resilience. • Bullying, discrimination, and disrupted schooling were associated with increased vulnerability. • Students’ self-reports showed higher predictive validity, while teacher reports aligned more closely with outcomes than caregiver reports. • Findings underscore the importance of inclusive, student-centered school environments.

High-performance dielectric materials are strategically vital for post-Moore microelectronics, high-power electrostatic capacitors, and flexible advanced electronics, yet their design is constrained by the intrinsic trade-off between breakdown strength (Eb) and relative permittivity (εr). Traditional high-throughput screening is limited to existing chemical spaces and cannot resolve the inherent orthogonality between key dielectric metrics and multiphysics coupling conflicts. This review systematically maps the paradigm shift in dielectric research from passive empirical screening toward generative, AI-driven autonomous discovery. We first examine the multiscale physical origins of the core dielectric performance trade-offs and the challenge of high-fidelity data scarcity, highlighting the role of physics-informed descriptors in bridging atomic-scale structures and macroscopic properties. We then survey the evolution of high-fidelity surrogate models and multiobjective optimization frameworks for efficient structure-property mapping followed by analysis of mainstream generative architectures (variational autoencoders, VAEs; generative adversarial networks, GANs; diffusion models), which enable the inverse design of dielectric polymers and inorganic crystals beyond known chemistries. Furthermore, we discuss the integration of self-driving autonomous laboratories and active learning strategies to close the feedback loop between computational prediction and experimental validation. Finally, we address the unresolved barriers in physics embedding, model explainability, and experimental synthesizability, outlining an actionable roadmap toward Physics-Sovereign AI and large language model (LLM)-driven digital scientists for dielectric innovation. This review provides a holistic, application-focused perspective on the transition from Edisonian trial-and-error approaches to a new era of rational, accelerated dielectric materials development and delivers a practical decision-making framework for researchers selecting AI tools for targeted dielectric design.

Upper tract urothelial carcinoma (UTUC) presents distinct diagnostic and therapeutic challenges because of its rarity, anatomic constraints, frequent understaging at biopsy, and risk of systemic recurrence after radical nephroureterectomy. Current perioperative management is driven primarily by clinicopathologic risk factors, which may be insufficient to identify occult molecular residual disease (MRD) or to determine which patients are most likely to benefit from systemic therapy. This narrative review summarizes available evidence on circulating tumor DNA (ctDNA) in UTUC and related urothelial carcinoma settings, classifies the level of evidence supporting each application, and proposes a research framework for prospective evaluation. The strongest UTUC-specific evidence supports ctDNA as a prognostic biomarker associated with recurrence risk, whereas predictive validity for selecting chemotherapy, immune checkpoint inhibitors, antibody-drug conjugates, targeted therapy, or surveillance intensity remains unproven. Evidence from muscle-invasive bladder cancer, including ctDNA-correlative and ctDNA-guided perioperative trials, provides biologic rationale but should not be directly translated into routine UTUC care without disease-specific validation. We outline key implementation questions, including target population, assay selection, timing, false-positive and false-negative results, lead-time bias, and integration of plasma ctDNA with utDNA. Prospective UTUC-specific trials are needed to determine whether ctDNA-guided perioperative strategies improve survival, reduce unnecessary toxicity, and are cost-effective.