OBJECTIVES:Pain is a common experience during childhood and adolescence influencing emotional well-being, social functioning, and academic performance. Currently, there is a lack of rigorously validated multidimensional tools specifically designed for adolescents that can simultaneously assess pain sensitivity, its social-emotional impact, and pain acceptance. METHODS:A sample of 836 adolescents completed the Adolescent Pain Sensitivity, Impact, and Acceptance Scale (APSIA) and the Chinese version of the Pain Sensitivity Questionnaire (PSQ) in Jiangxi Province, China (October 2023). The instrument was developed through experts' consultations and literature review, followed by a pilot test. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), validity analyses against the PSQ, K-means clustering, and structural equation modeling (SEM) were conducted using R. RESULTS:The final APSIA was reduced to 18 items with three factors via EFA and CFA: pain sensitivity (8 items), social-emotional impact (6 items), and pain acceptance (4 items). The model demonstrated acceptable fit (CFI = 0.938, RMSEA = 0.072) and internal consistency (α > 0.80). Validity analyses against the PSQ showed statistically significant but small group differences (p = 0.003, d = 0.21) and a weak association with limited explained variance (R2 = 0.031). Cluster analysis revealed three pain response profiles with distinct patterns in pain sensitivity, social-emotional impact, and pain acceptance. SEM revealed a strong association between pain acceptance and social-emotional impact (standardized β = 0.805, p < 0.001). Alternative directional models yielded equivalent model fit (ΔAIC < 0.001), suggesting that causal direction cannot be determined from cross-sectional data. CONCLUSION:The APSIA showed initial psychometric support for assessing pain experiences in adolescents. It may provide a useful foundation for early screening, psychological intervention, and personalized pain management. Future studies should evaluate its clinical utility, cross-cultural applicability, and long-term stability.

Background Pregnancy loss is common globally and may have long-term implications for women’s mental health. However, evidence across diverse sociocultural contexts remains limited, and its potential role in linking adverse childhood experiences (ACEs) to later-life depression is not well understood. Methods We conducted a cohort study using harmonised, deidentified data from three nationally representative longitudinal studies: the Health and Retirement Study (USA; 2000–2022), the English Longitudinal Study of Ageing (England; 2002–2018), and the China Health and Retirement Longitudinal Study (China; 2011–2020). Pregnancy loss (miscarriage, abortion, or stillbirth) was defined as any lifetime history (yes/no). ACEs were assessed using cumulative scores and domain-specific measures (threat, living instability, and household dysfunction). The primary outcome was clinically significant depressive symptoms measured using CES-D scales. Mixed-effects logistic regression models estimated associations between pregnancy loss and depression, adjusting for sociodemographic and health covariates. Population-attributable fractions (PAFs) were calculated using g-computation with 1000 bootstrap samples. Causal mediation analyses examined whether pregnancy loss mediated associations between ACEs and depression. Results Across all cohorts, pregnancy loss was associated with increased odds of depressive symptoms in later life. After adjustment for ACEs, the proportion of depression attributable to pregnancy loss ranged from 1·76% to 5·30%. Pregnancy loss partially mediated the association between cumulative ACE exposure and depressive symptoms, with mediation proportions ranging from 0·7% to 1·4%. Similar mediation patterns were observed across ACE domains, including threat, living instability, and household dysfunction. Findings were robust in sensitivity analyses. Conclusions Pregnancy loss is associated with a higher risk of depression in middle-aged and older women and contributes modestly to the burden of late-life depression. These results highlight the importance of adopting a life-course approach that integrates reproductive history and early-life adversity into mental health prevention, screening, and care strategies.

Autistic traits (ATs) are subclinical behavioral and cognitive characteristics associated with autism, prevalent in the general population. Studying individuals with high ATs offers valuable insights for autism research, particularly when utilizing larger samples with fewer confounding conditions. This study utilized the Chinese Version of the Comprehensive Autistic Trait Inventory (CATI-C) to assess 2214 university students at two time points, six months apart, to identify optimal methods for distinguishing high and low ATs. Internal consistency and test-retest reliability of the CATI-C were evaluated using Cronbach's alpha, Pearson's correlation, and Intraclass Correlation Coefficients (ICC). Kappa (kappa) consistency tests, combined with independent-sample t-tests, were used to compare four grouping methods: median, quartile, decile, and mean +/- 1 SD. Results indicated excellent internal consistency (Cronbach's alpha > 0.88) and strong test-retest reliability (Pearson's correlation and ICC > 0.67). Both Kappa and t-test analyses identified the mean +/- 1 SD (high ATs > 144; low ATs < 103) and decile (high ATs > 148; low ATs < 98) methods as the most stable and effective for distinguishing between high and low ATs. These findings provide critical guidance for optimizing the use of the CATI-C in future research on ATs in Chinese contexts.

In case of a disease, the family system can function as a burden or a resource and support system. The genogram (a visual depiction of the patient’s family relationships and other relevant information) helps the doctor to get a clear understanding of symptoms, life events, and diseases across three generations. The different phases of the family dialogue are: joining, context and mission clarification, questions about family dynamics and psychological resources and challanges, treatment planning, and conclusion of the interview. The following communication techniques can proof to be useful tools in conducting a family interview: direct disease-related questions, indirect or circular questions, and hypothetical questions.

Objective: Autistic traits (ATs), executive function, prosopagnosia, and social anxiety may interact dynamically: executive function variations might shape ATs and prosopagnosia; prosopagnosia could influence social cue processing, potentially affecting social anxiety; social anxiety, in turn, may impact executive function-forming interconnected relationships. Traditional methods fail to capture these complex dynamics, so this study aimed to use network analysis to explore their interrelations in college students and community members. Methods: A total of 1091 participants completed online self-report questionnaires. Network analysis was conducted to examine associations between the four variables, identify central/ bridge nodes, and compare differences between high and low AT groups. Results: Three trait communities were identified: (1) the social dimension of ATs + prosopagnosia + executive function; (2) "Social Anxiety"; (3) the non-social dimension of ATs + social camouflage. Social anxiety related to interacting with strangers (SOA2), social anxiety related to criticism and embarrassment (SOA4), and repetitive behaviors (REP) were the most central nodes; SOA2, executive function, and REP acted as bridge nodes. Significant differences in network patterns, edge strength, and node centrality were observed between high and low AT groups, with the high AT group showing weaker overall network strength. Conclusions: These findings provide valuable insights into the complex interactions among ATs, social anxiety, executive function, and prosopagnosia. The identification of key nodes and differences between AT groups in trait community connectivity has important implications for targeted interventions and personalized approaches in addressing these traits in individuals with varying levels of ATs.

Parenting stress is a central mechanism associated with parental and caregiving characteristics and children’s socioemotional development. However, the combined role of parental age and primary caregiver role in these associations remains unclear. This study applied network analysis to examine their associations with parenting stress and children’s emotional–behavioral difficulties. A cross-sectional study was conducted in October 2023 among 2454 parents from 22 schools in Xinzhou District, Shangrao City, Jiangxi Province, China. Participants completed questionnaires assessing parental age, primary caregiver role, parenting stress, and children’s emotional and behavioral difficulties. We conducted correlation analyses, ANOVAs, and network analysis to identify conditional associations and central nodes. Correlation analyses revealed relatively weak associations between parental age and children’s emotional and behavioral difficulties overall. Further group comparisons revealed that children of older fathers showed slightly higher levels of conduct problems, whereas older fathers themselves reported lower depression compared with younger age groups. Primary caregiver role also mattered: children raised primarily by mothers displayed fewer emotional difficulties, while those raised by fathers or other caregivers (e.g., grandparents) had relatively greater emotional-behavioral difficulties. Network analysis showed that difficult child characteristics and children’s emotional symptoms acted as central bridging nodes linking parenting stress and child outcomes, with parental distress showing high connectivity within the parenting stress community. Subgroup networks confirmed these patterns, with the difficult child consistently central across caregiver groups. Parental age and primary caregiving roles show distinct patterns of association with parenting stress and children’s emotional and behavioral difficulties. These patterns highlight the relevance of addressing both parental stress experiences and children’s emotional needs within family-oriented support approaches. Future longitudinal and cross-cultural research is needed to clarify these associations and to evaluate whether caregiver-specific interventions can enhance outcomes across diverse family contexts.

AimThis study aimed to analyze the influencing factors of non-suicidal self-injury (NSSI) in adolescents with depression.MethodsThis systematic review and meta-analysis were conducted in accordance with PRISMA guidelines. We systematically searched PubMed, Embase, Web of Science, the Cochrane Library, and the China National Knowledge Infrastructure (CNKI) for observational studies on factors influencing NSSI behavior in adolescents with depression published between 1 January 2010 and 7 May 2026. Two reviewers independently screened studies, extracted data, and assessed methodological quality using the Newcastle–Ottawa Scale. Meta-analyses were performed in RevMan 5.4 using mean differences (MDs) with 95% confidence intervals. Between-study heterogeneity was evaluated using the χ² test and I² statistic, and fixed- or random-effects models were applied as appropriate.ResultsIn total, 11 studies were included, involving 529 patients in the study group and 1,716 patients in the control group. The meta-analysis showed that, compared with the control group, the study group had significantly higher scores for interpersonal problems, negative emotions, alexithymia, and negative self-esteem [MD = 5.88, 95% CI (3.19, 8.57), P < 0.001], somatic symptom scores [MD = 5.20, 95% CI (–0.03, 10.43), P = 0.05], emotional deficit [MD = 6.82, 95% CI (0.32, 13.31), P = 0.04], Hamilton Depression Rating Scale scores [MD = 2.97, 95% CI (1.76, 4.18), P < 0.001], emotional abuse [MD = 2.10, 95% CI (1.06, 3.13), P < 0.001], physical abuse [MD = 0.64, 95% CI (0.08, 1.20), P = 0.025], sexual abuse [MD = 0.27, 95% CI (0.12, 0.42), P < 0.001], emotional neglect [MD = 1.67, 95% CI (0.37, 2.97), P = 0.009], physical neglect [MD = 0.81, 95% CI (0.43, 1.19), P < 0.001], and Childhood Trauma Questionnaire total scores [MD = 8.93, 95% CI (3.63, 14.24), P < 0.001].ConclusionAdolescents with depressive disorders and NSSI tend to exhibit more severe depressive symptoms, maladaptive emotional regulation patterns, negative coping styles, and higher levels of childhood trauma exposure than those without NSSI. These psychosocial and emotional factors may play important roles in the occurrence and maintenance of NSSI behaviors. Early psychological assessment and targeted intervention may help reduce the risk of NSSI in adolescents with depressive disorders.

OBJECTIVE:Childhood maltreatment (CM) and maladaptive emotion regulation (ER) are the distal and proximal factors associated with internalizing problems, respectively. However, previous studies on their relationships have often overlooked the co-occurrence of different types of CM and the concurrent use of various ER strategies. To address these gaps, this study aimed to investigate the associations among CM, ER strategies, and internalizing problems using network analysis. METHOD:This study involved 805 college students (438 female, Mage = 21.21 ± 1.80 years). Self-reported measures were used to assess CM, ER strategies, depressive symptoms, and anxiety symptoms. Undirected network analysis was employed to identify key connections among these factors. Bayesian network analysis was utilized to estimate a directed acyclic graph to explore the most likely putative causal associations. RESULTS:The results indicated that (a) emotional maltreatment was primarily associated with depressive symptoms, whereas physical and sexual abuse were predominantly linked to anxiety symptoms; (b) depressive and anxiety symptoms were related to maladaptive rather than adaptive ER strategies; (c) the impact of CM on ER strategies predominantly manifested in behavioral rather than cognitive ER strategies; and (d) emotional abuse played the most crucial role in the directed networks. CONCLUSIONS:These results clarified the interrelationship among CM, ER strategies, and internalizing problems, emphasizing the significant role of emotional abuse in internalizing problems and maladaptive ER. This provided valuable guidance for clinical interventions, school psychological counseling, and family education. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

ObjectiveThis study explores the differences in menopausal symptoms, illness conception, and health-seeking behaviors among Mosuo, Yi, and Han women in China, analyzes the key factors behind these differences, and conducts a longitudinal comparison.MethodsThis study collected data from Mosuo, Yi, and Han women in Yongning Township, Ninglang County, Yunnan Province, through a questionnaire survey. The instruments included the Kupperman Menopause Index (KMI), Depression-Anxiety-Stress Scale (DASS), Self-Rating Scale of Illness Conception and Health Seeking Behavior (SSICHSB) and General Self-Efficacy Scale (GSES). First, descriptive statistical analysis was conducted on the demographic characteristics and various indicators of the sample. Chi-square tests and one-way ANOVA were used to examine the differences in KMI and SSICHSB among the different ethnic groups. The KMI was used to assess menopausal symptoms, and multiple linear regression analysis was employed to identify the main factors influencing menopausal symptoms. A longitudinal comparison of data from 2012 and 2020 was performed to analyze the dynamic changes in KMI and SSICHSB of Mosuo and Han women.ResultsThe regression analysis identified stress, anxiety, and dysmenorrhea experience as risk factors, while self-efficacy served as a protective factor influencing menopausal symptoms. Both the menopausal symptoms and the scores for concerns and fears about illness among Mosuo women significantly decreased in 2020 compared to 2012 (p = 0.040, p = 0.010).ConclusionThe results provide an important basis for the development of culturally sensitive health interventions. Future public health strategies should consider cultural, social, and physiological factors to provide more effective health support and interventions for women from different ethnic groups.

In neuroscience and psychology research, the Pearson correlation coefficient is widely used for feature selection and model performance evaluation, particularly in studies examining relationships between brain activity and psychological behavior indices. However, when predicting psychological processes using connectome models, the Pearson correlation has three main limitations: (1) it struggles to capture the complexity of brain network connections; (2) it inadequately reflects model errors, especially in the presence of systematic biases or nonlinear error; and (3) it lacks comparability across datasets, with high sensitivity to data variability and outliers, potentially distorting model evaluation results. To better assess model performance, it is crucial to combine multiple evaluation metrics, such as mean absolute error (MAE) and root mean square error (MSE), which capture different aspects of model quality. Additionally, baseline comparisons, such as using the mean value or a simple linear regression (LR) model, provide an essential reference for evaluating the added value of more complex models. This approach offers a more robust and comprehensive analysis of functional connectomes and psychological processes.

PURPOSE:Non-suicidal self-injury is a common risk behavior in adolescence but is often difficult to detect. This study employs interpretable machine learning techniques to develop a classification model for adolescent non-suicidal self-injury and elucidate pertinent factors. Employing diverse algorithms, a comprehensive analysis is conducted to discern critical risk and protective elements within a large dataset, evaluating their alignment with the Integrated Theoretical Model. METHODS:In partnership with educational institutions in eastern China, this research compiled data on behaviors and correlated factors through the administration of questionnaires, incorporating demographic information and seven validated scales. Analytical models were built using six machine learning techniques: K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Light Gradient Boosting Machine, CatBoost, and eXtreme Gradient Boosting. RESULTS:The analysis included a total of 2989 valid responses samples. Among the algorithms, CatBoost demonstrated superior performance, evidenced by an AUPRC of 0.736 and an AUC of 0.863. SHAP visualization highlighted 23 important items. Exploratory factor analysis identified seven factors, designated as Situational Anxiety, Depressive Symptoms, Positive Daily Functioning, Negative Self Esteem, Self-Appraisal of Behavior, Bullying and Reactive Aggression, and Interpersonal Problems and Self-Acceptance. CONCLUSION:Leveraging multiple machine learning algorithms for a holistic item analysis, this research identifies critical risk and protective factors for non-suicidal self-injury, thus refining the Integrated Theoretical Model.

OBJECTIVE:Prediction is a fundamental cognitive process that facilitates adaptive interactions with the environment. The Prediction-Related Experiences Questionnaire (PRE-Q), a 19-item measure, assesses individuals' everyday predictive abilities. This study aimed to develop and validate a Chinese version (PRE-Q-C) by translating and adapting the original scale and examining its psychometric properties in a large Chinese sample. METHODS:The PRE-Q was translated and back-translated according to standard guidelines, followed by expert review and a comprehensibility test. A total of 2595 participants completed the PRE-Q-C and other related measures. Exploratory graph analysis (EGA) was used to identify the factor structure, confirmed via confirmatory factor analysis (CFA). Item response theory (IRT), internal consistency, test-retest reliability, and convergent validity were also evaluated. RESULTS:EGA suggested a 2-factor structure, comprising Sensory-Social prediction (7 items) and Motor prediction (6 items), which was subsequently supported by CFA. IRT analyses demonstrated that the PRE-Q-C items exhibited appropriate discrimination parameters. The scale showed high internal consistency (Cronbach's α = 0.85) and good test-retest reliability (r = 0.51, p < .001). Additionally, significant correlations with related psychological constructs supported the scale's convergent validity. CONCLUSION:The PRE-Q-C demonstrated strong psychometric properties, supporting its use as a reliable and valid instrument for assessing predictive processing in Chinese-speaking populations. These findings contribute to the understanding of individual differences in predictive abilities and their implications for mental health research.

Objective: Childhood sexual abuse (CSA) is a well-established risk factor for a range of psychological and behavioral issues, including non-suicidal self-injury (NSSI), making it a critical area of research for understanding adolescent mental health. Despite its prevalence, the mechanisms underlying their relationship remain insufficiently explored. This study aims to explore the relationship between CSA and NSSI behavior, as well as the mediating role of social avoidance and the moderating role of perceived family support. Method: 1737 Chinese adolescents with a mean age of 14.86 years (SD = 1.65) from hospitals' psychiatric departments in nine provinces of China were recruited to complete surveys on CSA experiences, NSSI behavior, and social support. Results: The results suggested that the effect of CSA on adolescent NSSI behavior was mediated by social avoidance, and the effect of CSA on social avoidance and NSSI behavior varied by the level of perceived family support. CSA led to social avoidance and increased risk for NSSI behavior. Higher levels of perceived family support had a protective effect, which reduced the motives of social avoidance and NSSI behavior. Conclusion: These findings revealed the risk of CSA and emphasized the role of social avoidance as an NSSI motive and the protective role of perceived family support for adolescents experiencing CSA. The clinical implications emphasize the need for interventions targeting family support and alternative coping strategies in the Chinese context. Additionally, future research should explore the complexity of intra- and extra-familial abuse and the roles of peer and professional support.

Objective: To assess structural solutions and measurement properties of the China Family Panel Studies (CFPS) version of Parental Bonding Instrument (PBI-CFPS) in Chinese adolescents. To illustrate the extensive parental bonding level of adolescents in China. Methods: We included 2671 subjects tested by the PBI-CFPS from the CFPS 2020 dataset. The random first split half was to explore and confirm the structural solutions of the PBI-CFPS by bootstrap Exploratory Graph Analysis (bootEGA). The other half was to compare and choose the reasonable solution by confirmatory factor analysis (CFA). The complete dataset was to evaluate the measurement invariances, internal consistency, and comprehensive parental bonding level. Results: The exploration and confirmation on all potential solutions of the PBI-CFPS have been conducted. The comprehensive assessments of this revised form of the PBI-CFPS under a two-factor solution with satisfactory measurement invariances and reliability indices have proven the availability of the application in Chinese adolescents. Geographic properties and other sub-characteristics were found in the parental bonding using the PBI-CFPS across the nation. Conclusions: The PBI-CFPS shows satisfactory psychometric properties for assessing parental bonding in adolescents. In addition, the parental bonding level of adolescents varies across China with the PBI-CFPS and more related outcomes can be discovered in the future.

Integrating artificial intelligence into psychological research represents a significant direction in contemporary psychology. Utilizing supervised and unsupervised machine learning techniques can further aid in understanding the nonlinear relationships of psychological concepts. In machine learning, variables, referred to as features, can encompass data from psychological scales, text, audio, and images. Current psychological research predominantly relies on frequentist approaches, where relationships between variables are typically based on regression, which often falls short in handling the nonlinear relationships of psychological characteristics. Therefore, we outline an innovative semi-automated workflow that empowers psychology researchers to leverage machine learning algorithms for intelligent model selection, facilitating the construction of more precise and insightful theoretical frameworks. This approach aims to achieve three primary research objectives: (1) automated hyperparameter tuning to attain optimal models; (2) identification of important features through interpretability techniques, facilitating feature selection based on calculated importance; (3) data-driven insights for theory building based on important features by integrating exploratory factor analysis with machine learning interpretability. In this paper, we provide an introduction to the basics of machine learning, describe the benefits of combining automated machine learning for researchers, and, using psychological resilience research as an example, offer a detailed annotated code workflow along with raw data. This low-code approach, designed with psychological research methodologies in mind, makes it highly accessible for psychological researchers.

Objects Childhood trauma is an early pathogenic factor that increases individuals' vulnerability to mental illness. This systematic review aims to explore the evidence regarding the association between childhood trauma and the subsequent occurrence of anxiety disorders. Methods Embase, Scopus, and PubMed databases were searched for peer-reviewed longitudinal cohort studies published in English between January 1, 1995, and November 15, 2022. These studies investigated the association between childhood traumas and later diagnose of anxiety disorders. Including studies in one previous meta-analysis and two umbrella reviews, a total of 27 manuscripts were retained in this meta-analysis. Results The evidence strongly revealed that childhood trauma exposure has detrimental effects on anxiety disorders, consistent from youth to adulthood. Moreover, these effects remained consistent across various types of anxiety disorders, including generalized anxiety disorder and social anxiety disorder, agoraphobia, specific phobia, panic attack and panic disorder. Importantly, exposure to childhood trauma, including maternal dysfunction, paternal dysfunction, physical abuse, sexual abuse, emotional abuse and bullying, was associated with later development of anxiety disorders, with bullying and maternal dysfunction ranking highest. Conclusions This meta-analysis underscores that childhood trauma exposure significantly increases the risk of anxiety disorders across different age groups and types of anxiety disorders. Various forms of childhood trauma, including maternal dysfunction, paternal dysfunction, physical abuse, sexual abuse, emotional abuse and bullying, are consistently associated with later development of anxiety disorders, highlighting the importance of early intervention and support in preventing anxiety disorders.

Non-suicidal self-injury (NSSI) refers to intentionally harming one's own body tissue without intending to die or cause injury to others. Common forms of NSSI include cutting, burning, scratching, hitting oneself, and pulling out hair. There are various etiology for NSSI behaviour, which ranges from that of emotional dysregulation to those of cognitive factors. We are particularly interested in the development of a Cognition-Emotion-Behaviour model of NSSI. However, there exist various states concerning cognition (713 functions), emotion (558 states), and NSSI behaviors (81 ones), according to the well-known medical terminologies SNOMED CT. It is non-trivial to know which cognition functions and which emotion states would lead to which kinds of NSSI behaviours. In this paper, we propose a Cognition-Emotion-Behaviour model of NSSI by using an approach of Knowledge Graphs with semantic analysis on the literature mining of NSSI. We will show how cognition, emotion, and NSSI behaviours can be explored by using this knowledge graph approach.

BackgroundAlthough brain-derived neurotrophic factor (BDNF)has garnered extensive attention as a neuroendocrine marker in schizophrenia (SZ), its clinical utility remains limite due to inconsistent findings.MethodsTo address this gap, serum samples were collected from 24 female patients with SZ and 25 healthy controls. The metabolic profiling was performed using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) to capture abroad range of metabolites.ResultsOur results revealed that BDNF is not a robust discriminatory biomarker. Marked differences in metabolic profiles were identified between patients with SZ and healthy individuals. The GC-MS analysis revealed significant differences in 79 metabolites; while the LC-MS analysis identified 419 significantly differential metabolites. Functional analysis reveals that these differential metabolites predominantly contribute to metabolic and neuro-related processes. Our findings demonstrate that norepinephrine and melatonin, two additional neuroendocrine compounds, are significantly elevated in patients with SZ compared to healthy controls. Notably, their higher areas under the curve (AUC) values compared to BDNF highlight their potential as more reliable biomarkers for SZ.ConclusionThis study offers valuable insights into the altered metabolic patterns of female patients with SZ and establishes melatonin and norepinephrine as promising neuroendocrine biomarkers, underscoring their diagnostic value and role in the neuroendocrine regulation of mental disorders.