Calibrating Items Using an Unfolding Model of Item Response Theory: The Case of the Trait Personality Questionnaire 5 (TPQue5)

Unfolding item response theory (IRT) models are important alternatives to dominance IRT models in describing the response processes on self-report tests. Their usage is common in personality measures, since they indicate potential differentiations in test score interpretation. This paper aims to gain a better insight into the structure of trait personality, by investigating whether the dominance or alternatively the unfolding IRT model are better descriptors of the response processes on a personality measure constructed under the dominance response theorem. For the assessment of the dominant model, the Graded Response Model (GRM) is used; while for the unfolding model, the Generalized Graded Unfolding Model (GGUM) was examined. All analyses are conducted with the freely available R. A sample of 1340 Greek adults, employed in private and public organizations, fulfilled the Trait Personality Questionnaire 5 short-form (TPQue5). Findings contradict previous research on trait personality. In accordance to the construction method employed, the TPQue5 items are best understood by monotonically increasing item response functions (IRFs). Individuals responding to the TPQue5 increase their probability of endorsing its items as their trait level increases; this stands for all personality dimensions, although Openness to Experience exhibited mixed type of item response patterns. Further research directions, implications and limitations are also discussed.

Exploring the effectiveness of circuit training rehabilitation on balance, gait, and fatigue in multiple sclerosis: a systematic review and meta-analysis

BACKGROUND

People with Multiple Sclerosis (PwMS) often experience imbalance, gait dysfunction, and fatigue. Circuit Training (CT) can be viable for improving balance, gait, and fatigue in MS. To the author's knowledge, no studies have systematically reviewed the existing literature evaluating the effectiveness of CT in PwMS.

OBJECTIVES

To investigate the effectiveness of CT in improving balance, gait, and reducing fatigue in PwMS and provide a quantitative and qualitative synthesis of Randomized Controlled Trials (RCTs).

DATA SOURCES

MEDLINE, Cochrane Central Register of Controlled Trials, EMBASE, CINAHL, Google Scholar, and PEDro Database (Dec 2021 to May 2024).

STUDY SELECTION

RCTs using CT in PwMS including balance, gait, or fatigue outcomes.

DATA SYNTHESIS

Search inclusion criteria were: i) available full text, ii) CT rehabilitation, iii) balance, gait, or fatigue measured as outcomes, and iv) articles in English. Full text articles were analyzed by two screeners. If there was disagreement regarding inclusion, a further reviewer was consulted. No discrepancies were found.

RESULTS

We identified 878 studies, 14 studies were eligible including 716 PwMS with a mean (standard deviation) age of 49.9 (10.9) years, disease duration of 10.8 (7.2) years, and Expanded Disability Status Scale score of 4.3 (0.9) points. RevMan 5.4.1 was used to run the meta-analysis. We found a significant overall effect on Berg Balance Scale (Mean Difference (MD) = 6.07 points, 95%CI:1.40,10.75; p = 0.01) and in Fatigue Severity Scale (MD = 0.98 points, 95%CI:0.30,1.66; p = 0.005) in favor of CT. We did not find a significant effect in Timed Up and Go (MD = 0.46 second, 95%CI:-0.04,0.96; p = 0.07), in Six-Minute Walk Test (MD = 17.46 m, 95%CI:-8.06,42.97; p = 0.18), and in Modified Fatigue Impact Scale (MD = 3.34 points, 95%CI:-0.41,7.09; p = 0.08) in favor of CT. We assessed methodological quality using RoB 2.0, and quality of evidence using GRADE.

LIMITATIONS

Small number of studies, all identifying having some risk of bias.

CONCLUSION

Circuit training can have positive effects on PwMS in terms of increasing balance, gait, and reducing fatigue. Further research is needed.

SYSTEMATIC REVIEW REGISTRATION NUMBER

PROSPERO CRD42021286834. CONTRIBUTION OF THE PAPER.

Model-implied simulation-based power estimation for correctly specified and distributionally misspecified models: Applications to nonlinear and linear structural equation models

Closed-form (asymptotic) analytical power estimation is only available for limited classes of models, requiring correct model specification for most applications. Simulation-based power estimation can be applied in almost all scenarios where data following the model can be estimated. However, a general framework for calculating the required sample sizes for given power rates is still lacking. We propose a new model-implied simulation-based power estimation (MSPE) method for the z-test that makes use of the asymptotic normality property of estimates of a wide class of estimators, the M-estimators, and give theoretical justification for the approach. M-estimators include maximum-likelihood, least squares estimates and limited information estimators, but also estimators used for misspecified models, hence, the new simulation-based power modeling method is widely applicable. The MSPE employs a parametric model to describe the relationship between power and sample size, which can then be used to determine the required sample size for a specified power rate. We highlight its performance in linear and nonlinear structural equation models (SEM) for correctly specified models and models under distributional misspecification. Simulation results suggest that the new power modeling method is unbiased and shows good performance with regard to root mean squared error and type I error rates for the predicted required sample sizes and predicted power rates, outperforming alternative approaches, such as the naïve approach of selecting a discrete selection of sample sizes with linear interpolation of power or simple logistic regression approaches. The MSPE appears to be a valuable tool to estimate power for models without an (asymptotic) analytical power estimation.

Evaluating the role of biological age in osteoporosis risk among middle-aged and older adults: A nationwide perspective

OBJECTIVES

This study aimed to investigate the association between biological age acceleration and osteoporosis (OP) risk in middle-aged and older adults using data from the National Health and Nutrition Examination Survey (NHANES). The research focused on analyzing the relationship between two biological aging metrics, Klemera-Doubal Method Age (KDMAge) and Phenotypic Age (PhenoAge), and OP risk.

METHODS

The study analyzed data from NHANES, which included 6550 participants aged 50 and above from survey cycles 2005-2010 and 2017-2018. Linear and logistic regression were used to investigate the relationship between biological age acceleration (KDMAgeAccel and PhenoAgeAccel) and OP. Subgroup analysis was performed by age, gender and other factors. Multivariable Cox regression analysis yielded Hazard Ratios (HRs) relating biological age acceleration to mortality were evaluated. The study also considered the mediating roles of body mass index (BMI).

RESULTS

KDMAgeAccel (odds ratio [OR] = 2.34, 95 % CI, 1.72-3.18) and PhenoAgeAccel (OR = 2.03, 95 % CI, 1.48-2.78) were significantly associated with increased OP risk and reduced bone mineral density (BMD). Specifically, higher KDMAgeAccel and PhenoAgeAccel were linked to higher OP prevalence and lower BMD at multiple sites. Subgroup analyses indicated that the association between accelerated biological age acceleration and OP risk was consistent across different demographics. Mediation analysis revealed that BMI partially mediated the relationship between accelerated biological age and OP, although other mechanisms are likely involved. Statistical analysis indicated that individuals with higher biological age metrics had increased mortality risk related to OP.

CONCLUSION

The findings suggest that accelerated biological age is a robust predictor of OP risk and related mortality. KDMAgeAccel and PhenoAgeAccel could serve as valuable biomarkers for identifying individuals at high risk for OP, guiding preventive strategies.

The Relationship Between Posttraumatic Stress Disorder and Alcohol Misuse and Smoking Among Aging Men Who Have Sex With Men: No Evidence of Exercise or Volunteering Impact

OBJECTIVES

To determine if the association between posttraumatic stress disorder (PTSD) and substance use (alcohol misuse or smoking tobacco) is mediated/moderated by exercise or volunteering among aging (≥40 years) men who have sex with men (MSM), and if this mediation/moderation differs by HIV serostatus.

METHODS

Multicenter AIDS Cohort Study data were used. Three datasets with PTSD measured during different time periods (10/1/2017-3/31/2018, 898 men; 4/1/2018-9/30/2018, 890 men; 10/1/2018-3/31/2019, 895 men) were analyzed. Longitudinal mediation analyses estimated the mediation effect of exercise and volunteering on the outcomes.

RESULTS

Nine percent of MSM had evidence of PTSD. There was no statistically significant mediation effect of exercise or volunteering regardless of substance use outcome. The odds of smoking at a future visit among MSM with PTSD were approximately double those of MSM without PTSD. Results did not differ by HIV serostatus.

DISCUSSION

There is a particular need for effective smoking cessation interventions for aging MSM with PTSD.

Charting a path to health: The empowering influence of self-efficacy for the self-management of type 1 diabetes in children and adolescents

Type 1 diabetes (T1D) in children and adolescents requires a lifelong commitment to disease control, which involves insulin treatment and constant blood glucose monitoring. Framed by Albert Bandura's self-efficacy theory, we focused on analysing the impact of domain-specific self-efficacy for T1D control in children and adolescents and its relationship with different indicators of glycaemic control over time. The study included 205 participants (56.1% male), including 51.7% children and 48.3% adolescents aged 6-18 (M = 13.27, SD = 3.66) years in four longitudinal phases (6 months between phases). The results revealed that higher self-efficacy predicted better health outcomes, with more time spent actively monitoring glucose and more time in the target range. The positive effect of self-efficacy was observed to be maintained over time. This study underscores the importance of taking into account the developmental timing in the onset of T1D. There was a significant relationship between self-efficacy and glucose indicators in adolescents. Although their glucose indicators were worse, self-efficacy became more relevant as they moved from parental management to the self-management of T1D. Implications of the results show the positive effect of self-efficacy on health per glucose indicators, thus suggesting interventions that promote self-efficacy in this population.

Interactions between latent variables in count regression models

In psychology and the social sciences, researchers often model count outcome variables accounting for latent predictors and their interactions. Even though neglecting measurement error in such count regression models (e.g., Poisson or negative binomial regression) can have unfavorable consequences like attenuation bias, such analyses are often carried out in the generalized linear model (GLM) framework using fallible covariates such as sum scores. An alternative is count regression models based on structural equation modeling, which allow to specify latent covariates and thereby account for measurement error. However, the issue of how and when to include interactions between latent covariates or between latent and manifest covariates is rarely discussed for count regression models. In this paper, we present a latent variable count regression model (LV-CRM) allowing for latent covariates as well as interactions among both latent and manifest covariates. We conducted three simulation studies, investigating the estimation accuracy of the LV-CRM and comparing it to GLM-based count regression models. Interestingly, we found that even in scenarios with high reliabilities, the regression coefficients from a GLM-based model can be severely biased. In contrast, even for moderate sample sizes, the LV-CRM provided virtually unbiased regression coefficients. Additionally, statistical inferences yielded mixed results for the GLM-based models (i.e., low coverage rates, but acceptable empirical detection rates), but were generally acceptable using the LV-CRM. We provide an applied example from clinical psychology illustrating how the LV-CRM framework can be used to model count regressions with latent interactions.

Using the multidimensional nominal response model to model faking in questionnaire data: The importance of item desirability characteristics

Faking in self-report personality questionnaires describes a deliberate response distortion aimed at presenting oneself in an overly favorable manner. Unless the influence of faking on item responses is taken into account, faking can harm multiple psychometric properties of a test. In the present article, we account for faking using an extension of the multidimensional nominal response model (MNRM), which is an item response theory (IRT) model that offers a flexible framework for modeling different kinds of response biases. Particularly, we investigated under which circumstances the MNRM can adequately adjust substantive trait scores and latent correlations for the influence of faking and examined the role of variation in the way item content is related to social desirability (i.e., item desirability characteristics) in facilitating the modeling of faking and counteracting its detrimental effects. Using a simulation, we found that the inclusion of a faking dimension in the model can overall improve the recovery of substantive trait person parameters and latent correlations between substantive traits, especially when the impact of faking in the data is high. Item desirability characteristics moderated the effect of modeling faking and were themselves associated with different levels of parameter recovery. In an empirical demonstration with N = 1070 test-takers, we also showed that the faking modeling approach in combination with different item desirability characteristics can prove successful in empirical questionnaire data. We end the article with a discussion of implications for psychological assessment.

Punishment-resistant alcohol intake is mediated by the nucleus accumbens shell in female rats

Alcohol use is widespread across many societies. While most people can control their alcohol use, a vulnerable sub-population develops alcohol use disorder, characterized by continued alcohol use despite negative consequences. We used a rat model of alcohol self-administration despite negative consequences to identify brain activity associated with this addiction-like behaviour. We and others have previously shown that response-contingent punishment of alcohol self-administration with mild footshock reliably identifies two sub-populations. One group substantially decreases alcohol self-administration in the face of punishment (punishment-sensitive, controlled) and another group continues alcohol self-administration despite negative consequences (punishment-resistant, addiction-like behaviour). In this study, we aimed to validate this model in females and identify associated brain regions. We trained Long-Evans outbred rats (n = 96) to self-administer 20% ethanol, and then introduced response-contingent footshock. We found that female rats consumed more alcohol in unpunished and punished sessions compared to male rats. In one group of rats (n = 24, m/f), we identified neuronal activity associated with punishment-resistant alcohol self-administration using the neurobiological marker of activity cFos. We found lower cFos expression in NAcSh associated with punishment-resistant alcohol self-administration. In another group of rats (n = 72, m/f), we used chemogenetic inhibition of NAcSh during punished alcohol self-administration. We found that chemogenetic NAcSh inhibition had no effect on unpunished alcohol self-administration but selectively increased punished alcohol self-administration in punishment-resistant female rats. These results indicate that more female rats develop punishment-resistant alcohol consumption, and that NAcSh hypofunction may underlie this phenotype.

Age-related effects on a hierarchical structure of canine cognition

The current study investigates whether there are statistically independent age-related influences on the canine cognitive structure and how individual factors moderate cognitive aging on both cross-sectional and longitudinal samples. A battery of seven tasks was administered to 129 pet dogs, on which exploratory and confirmatory factor analyses were employed to unveil the correlational structure underlying individual differences in cognitive performance. The best-fitting model featured a hierarchical structure with two first-order cognitive domains (individual problem solving, learning) and a second-order common factor. These higher order factors exhibited consistency over a period of at least 2.5 years. External validation linked the common factor positively to discrimination and reversal learning performance, exploration, neophilia, activity/excitability, and training level while negatively to cognitive dysfunction symptoms, suggesting that it is a good candidate for a general cognitive factor (canine g). Structural equation models identified three distinct age-related influences, operating on associative learning, on memory, and on canine g. Health status moderated the negative age-canine g relationship, with a stronger association observed in dogs with poorer health status, and no relationship for dogs in good health. On a longitudinal sample (N = 99), we showed that the direction and magnitude of change in canine g over up to 3 years is affected by various interactions between the dogs' age, communication score, baseline performance, and time elapsed since the baseline measurement. These findings underscore the presence of a general cognitive factor in dogs and reveal intriguing parallels between human and canine aging, affirming the translational value of dogs in cognition and aging research.

Careless responding detection revisited: Accuracy of direct and indirect measures

To screen for careless responding, researchers have a choice between several direct measures (i.e., bogus items, requiring the respondent to choose a specific answer) and indirect measures (i.e., unobtrusive post hoc indices). Given the dearth of research in the area, we examined how well direct and indirect indices perform relative to each other. In five experimental studies, we investigated whether the detection rates of the measures are affected by contextual factors: severity of the careless response pattern, type of item keying, and type of item presentation. We fully controlled the information environment by experimentally inducing careless response sets under a variety of contextual conditions. In Studies 1 and 2, participants rated the personality of an actor that presented himself in a 5-min-long videotaped speech. In Studies 3, 4, and 5, participants had to rate their own personality across two measurements. With the exception of maximum longstring, intra-individual response variability, and individual contribution to model misfit, all examined indirect indices performed better than chance in most of the examined conditions. Moreover, indirect indices had detection rates as good as and, in many cases, better than the detection rates of direct measures. We therefore encourage researchers to use indirect indices, especially within-person consistency indices, instead of direct measures.

State program enables the identification of factors associated with naloxone awareness, self-efficacy, and use for overdose reversal: A cross-sectional, observational study in an urban emergency department population

INTRODUCTION

To assist the state of Ohio in addressing the opioid epidemic, the Ohio Attorney General appointed experts in a variety of academic disciplines to the Scientific Committee on Opioid Prevention and Education (SCOPE). The focus of SCOPE is the application of scientific principles to the development of prevention and educational strategies for reducing substance use disorder and related harms (e.g., promoting naloxone awareness). Naloxone awareness is a step in the naloxone cascade, which is a useful model for understanding the sequential steps laypeople must take to prepare themselves to intervene using naloxone; other steps include training and previous administration experience. Prior work has explored correlates of these steps among individuals with risky substance use, but fewer studies have focused on broader populations containing potential bystanders (e.g., family and community members).

METHODS

This study was a secondary data analysis of patients from three urban emergency departments. Subsamples differed across five models (n = 479-1208) and included opioid-exposed and -naïve participants. Logistic regression characterized clinically useful sociodemographic predictors (e.g., race, ethnicity, education, employment, housing status) of naloxone awareness, self-efficacy (which relates to training), and previous-overdose administration. Two additional logistic regressions tested associations between risk factors for witnessing an opioid overdose and two cascade steps (awareness and self-efficacy).

RESULTS

Non-White race, Hispanic ethnicity, and lower education predicted not being aware of naloxone; non-White race also predicted lower naloxone self-efficacy, and older age predicted lack of previous-overdose administration. Having family members with risky opioid use was heavily associated with awareness, while personal substance-use behaviors and previous overdose witnessing were associated with both awareness and higher naloxone self-efficacy.

CONCLUSIONS

Characteristics associated with lower likelihood of completing each cascade step highlight opportunities for targeted interventions. Specifically, findings indicated the importance of expanding naloxone education and training programs to more diverse populations and to family members of individuals with risky opioid use. Further, these findings demonstrate how a state-funded program such as SCOPE can have a positive impact on identifying strategies that may assist in reducing mortality associated with opioid overdose.

Latent Variable Forests for Latent Variable Score Estimation

We develop a latent variable forest (LV Forest) algorithm for the estimation of latent variable scores with one or more latent variables. LV Forest estimates unbiased latent variable scores based on confirmatory factor analysis (CFA) models with ordinal and/or numerical response variables. Through parametric model restrictions paired with a nonparametric tree-based machine learning approach, LV Forest estimates latent variable scores using models that are unbiased with respect to relevant subgroups in the population. This way, estimated latent variable scores are interpretable with respect to systematic influences of covariates without being biased by these variables. By building a tree ensemble, LV Forest takes parameter heterogeneity in latent variable modeling into account to capture subgroups with both good model fit and stable parameter estimates. We apply LV Forest to simulated data with heterogeneous model parameters as well as to real large-scale survey data. We show that LV Forest improves the accuracy of score estimation if parameter heterogeneity is present.

A network analysis of daily stressors, subjective well-being, psychological distress and psychological capital during the COVID-19 pandemic

BACKGROUND

There is already considerable evidence suggesting the potential existence of a comprehensive system that reflects how individuals utilize psychological capital (PsyCap) to preserve mental health amid daily stressors during the COVID-19 pandemic. To explore the underlying mechanism of this system, the current study is the first to use network analysis showing the dimension-level correlation patterns of daily stressors, subjective well-being (SWB), psychological distress and PsyCap during the pandemic.

METHOD

We recruited 1556 participants in China and assessed daily stressors, SWB, psychological distress, and PsyCap through self-report questionnaires. A dimension-level network analysis was conducted to identify key dimensions and their associations. Relative importance analysis examined the contribution of each PsyCap dimension to SWB and psychological distress.

RESULT

Depression, anxiety and stress demonstrated high strength, and life satisfaction exhibited the highest bridge strength in the network. The great majority dimensions of daily stressors had positive connections with stress and anxiety, while financial restrictions and dissatisfaction with education/occupation had negative connections with life satisfaction. Self-efficacy and optimism were positively connected with life satisfaction. Hope and resilience were negatively connected with stress, as well as hope and optimism were negatively connected with depression. Optimism and self-efficacy made the highest contribution to SWB, while resilience made the highest contribution to psychological distress among PsyCap dimensions.

CONCLUSION

The findings elucidate the intricate relationship between daily stressors, SWB, psychological distress and PsyCap during the pandemic. Targeted interventions focusing on the specific PsyCap dimensions may enhance mental health outcomes in the post-COVID-19 era.

A Good check on the Bayes factor

Bayes factor hypothesis testing provides a powerful framework for assessing the evidence in favor of competing hypotheses. To obtain Bayes factors, statisticians often require advanced, non-standard tools, making it important to confirm that the methodology is computationally sound. This paper seeks to validate Bayes factor calculations by applying two theorems attributed to Alan Turing and Jack Good. The procedure entails simulating data sets under two hypotheses, calculating Bayes factors, and assessing whether their expected values align with theoretical expectations. We illustrate this method with an ANOVA example and a network psychometrics application, demonstrating its efficacy in detecting calculation errors and confirming the computational correctness of the Bayes factor results. This structured validation approach aims to provide researchers with a tool to enhance the credibility of Bayes factor hypothesis testing, fostering more robust and trustworthy scientific inferences.

Estimating power in complex nonlinear structural equation modeling including moderation effects: The powerNLSEM R-package

The model-implied simulation-based power estimation (MSPE) approach is a new general method for power estimation (Irmer et al., 2024). MSPE was developed especially for power estimation of non-linear structural equation models (SEM), but it also can be applied to linear SEM and manifest models using the R package powerNLSEM. After first providing some information about MSPE and the new adaptive algorithm that automatically selects sample sizes for the best prediction of power using simulation, a tutorial on how to conduct the MSPE for quadratic and interaction SEM (QISEM) using the powerNLSEM package is provided. Power estimation is demonstrated for four methods, latent moderated structural equations (LMS), the unconstrained product indicator (UPI), a simple factor score regression (FSR), and a scale regression (SR) approach to QISEM. In two simulation studies, we highlight the performance of the MSPE for all four methods applied to two QISEM with varying complexity and reliability. Further, we justify the settings of the newly developed adaptive search algorithm via performance evaluations using simulation. Overall, the MSPE using the adaptive approach performs well in terms of bias and Type I error rates.

Longitudinal and Gender Measurement Invariance of the General Health Questionnaire-12 (GHQ-12) From Adolescence to Emerging Adulthood

Psychological distress often onsets during adolescence, necessitating an accurate understanding of its development. Assessing change in distress is based on the seldom examined premise of longitudinal measurement invariance (MI). Thus, we used three waves of data from Next Steps, a representative cohort of young people in the UK (N = 13,539) to examine MI of the General Health Questionnaire-12 (GHQ-12). We examined MI across time and gender from ages 15 to 25 in four competing latent models: (a) a single-factor model, (b) a three-factor correlated model, (c) a bifactor model of "general distress" and two orthogonal specific factors capturing positive and negative wording, and (d) a single-factor model including error covariances of negatively phrased items. We also tested acceptability of assumptions underlying sum score models. For all factor models, residual MI was confirmed from ages 15 to 25 years and across gender. The bifactor model had the best fit. While sum score model fit was not unequivocally acceptable, most mean differences across time and gender were equivalent across sum scores and latent difference scores. Thus, GHQ-12 sum scores may be used to assess change in psychological distress in young people. However, latent scores appear more accurate, and model fit can be improved by accounting for item wording.

Does Being Over 60 Years Old at Index Offense Impact Sexual Recidivism Risk? A Large-Scale Comparison of Men Released Over the Age of 60

Research has consistently found risk for sexual recidivism drops as men convicted of a sexual offense age. One question that remains, however, is whether this age-related reduction in risk applies to men who commit their sexual offenses at an older age. The current study examined this question in a sample of 939 men who were screened for civil management and released to the community (mean follow-up = 6.33 years, SD = 3.98 years), by comparing the sexual recidivism rates of (a) 238 men convicted of sexual offenses committed over the age of 60 (Over-60 Index group), and (b) 701 men who committed sexual offenses under the age of 60 but who were released to the community over the age of 60 (Under-60 Index group). Even though the Under-60 Index group was significantly higher risk at release, the Over-60 Index group sexually recidivated at a significantly higher rate, a difference largely driven by the small group of men who had committed sexual offenses both under and over the age of 60. The Static-99R was a strong predictor of sexual recidivism regardless of age at sexual offending (i.e., just under 60, just over 60, or both under and over 60). The 2021 Routine Samples norms significantly overpredicted sexual recidivism for the Under-60 Index group and significantly underpredicted sexual recidivism for the Over-60 Index group, indicating that the use of the 2016 High Risk/High Needs norms could be more appropriate for the Over-60 Index group, particularly those men who sexually offended under the age of 60 before sexually offending over the age of 60.

A tutorial on Bayesian structural equation modelling: Principles and applications

This paper explores the utilisation of Bayesian structural equation modelling (BSEM) in psychology, highlighting its advantages over frequentist methods for handling complex models and small sample sizes. Basic concepts and fundamental issues relevant to BSEM are introduced, such as prior setting, model convergence, and model fit evaluation and so on. The paper also provides illustrative examples of commonly employed BSEMs, including confirmatory factor analysis (CFA) models, mediation models and multigroup CFA models, accompanied by empirical data and computer codes to facilitate implementation. Our goal is to provide researchers with novel ideas for empirical research and equip them to overcome challenges inherent to traditional methods. As BSEM continues to gain traction in various fields, we anticipate its development will feature improved methods, techniques and reporting standards.

Topological properties of psychopathological networks of healthy and disordered individuals across mental disorders

The identification of psychopathological markers has been the focus of several scientific fields. The results were inconsistent due to lack of a clear nosology. Network analysis, focusing on the interactions between symptoms, provided important insights into the nosology of mental disorders. These interactions originate several topological properties that could constitute markers of psychopathology. One of these properties is network connectivity, which has been explored in recent years. However, the results have been inconsistent, and the topological properties of psychopathological networks remain largely unexplored and unknown. We compared several topological properties (i.e., connectivity, average path length, assortativity, average degree, modularity, global clustering) of psychopathological networks of healthy and disordered participants across depression (N = 2830), generalized anxiety (N = 13,463), social anxiety (N = 12,814), and obsessive-compulsive disorder (N = 16,426). Networks were estimated using Bayesian Gaussian Graphical Models. The Janson-Shannon measure of divergence was used to identify differences between the network properties. Network connectivity distinguished healthy and disordered participants' networks in all disorders. However, in depression and generalized anxiety, network connectivity was higher in healthy participants. The presence and number of motifs also distinguished the networks of healthy and disordered participants. Other topological properties (i.e., modularity, clustering, average path length and average degree) seem to be disorder-specific. The psychopathological significance of network connectivity must be clarified. Some topological properties of psychopathological networks are promising markers of psychopathology and may contribute to clarifying the nosology of mental disorders.

A Bifactor Evaluation of Self-Report and Clinician-Administered Measures of PTSD in Veterans

The PTSD Checklist for DSM-5 (PCL-5) and the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5) are two of the most widely used and well-validated PTSD measures providing total and subscale scores that correspond with DSM-5 PTSD symptoms. However, there is little information about the utility of subscale scores above and beyond the total score for either measure. The current study compared the proposed DSM-5 four-factor model to a bifactor model across both measures using a sample of veterans (N = 1,240) presenting to a Veterans Affairs (VA) PTSD specialty clinic. The correlated factors and bifactor models for both measures evidenced marginal-to-acceptable fit and were retained for further evaluation. Bifactor specific indices suggested that both measures exhibited a strong general factor but weak lower-order factors. Structural regressions revealed that most of the lower-order factors provided little utility in predicting relevant outcomes. Although additional research is needed to make definitive statements about the utility of PCL-5 and CAPS-5 subscales, study findings point to numerous weaknesses. As such, caution should be exercised when using or interpreting subscale scores in future research.

Enhancing the Detection of Social Desirability Bias Using Machine Learning: A Novel Application of Person-Fit Indices

Social desirability bias (SDB) is a common threat to the validity of conclusions from responses to a scale or survey. There is a wide range of person-fit statistics in the literature that can be employed to detect SDB. In addition, machine learning classifiers, such as logistic regression and random forest, have the potential to distinguish between biased and unbiased responses. This study proposes a new application of these classifiers to detect SDB by considering several person-fit indices as features or predictors in the machine learning methods. The results of a Monte Carlo simulation study showed that for a single feature, applying person-fit indices directly and logistic regression led to similar classification results. However, the random forest classifier improved the classification of biased and unbiased responses substantially. Classification was improved in both logistic regression and random forest by considering multiple features simultaneously. Moreover, cross-validation indicated stable area under the curves (AUCs) across machine learning classifiers. A didactical illustration of applying random forest to detect SDB is presented.

Comparing Accuracy of Parallel Analysis and Fit Statistics for Estimating the Number of Factors With Ordered Categorical Data in Exploratory Factor Analysis

Determining the number of factors in exploratory factor analysis (EFA) is crucial because it affects the rest of the analysis and the conclusions of the study. Researchers have developed various methods for deciding the number of factors to retain in EFA, but this remains one of the most difficult decisions in the EFA. The purpose of this study is to compare the parallel analysis with the performance of fit indices that researchers have started using as another strategy for determining the optimal number of factors in EFA. The Monte Carlo simulation was conducted with ordered categorical items because there are mixed results in previous simulation studies, and ordered categorical items are common in behavioral science. The results of this study indicate that the parallel analysis and the root mean square error of approximation (RMSEA) performed well in most conditions, followed by the Tucker-Lewis index (TLI) and then by the comparative fit index (CFI). The robust corrections of CFI, TLI, and RMSEA performed better in detecting misfit underfactored models than the original fit indices. However, they did not produce satisfactory results in dichotomous data with a small sample size. Implications, limitations of this study, and future research directions are discussed.

Exploring symptom clusters in Chinese patients with peritoneal dialysis: a network analysis

BACKGROUND

In recent years, the research on symptom management in peritoneal dialysis (PD) patients has shifted from a single symptom to symptom clusters and network analysis. This study collected and evaluated unpleasant symptoms in PD patients and explored groups of symptoms that may affect PD patients with a view to higher symptom management.

METHODS

The symptoms of PD patients were measured using the modified Dialysis Symptom Index. The symptom network and node characteristics were assessed by network analysis, and symptom clusters were explored by factor analysis.

RESULTS

In this study of 602 PD patients (mean age 47.8 ± 16.8 years, 47.34% male), most had less than 2 years of dialysis experience. Five symptom clusters were obtained from factor analysis, which were body symptom cluster, gastrointestinal symptom cluster, mood symptom cluster, sexual disorder symptom cluster, and skin-sleep symptom cluster. Itching and decreased interest in sex may be sentinel symptoms, and being tired or lack of energy and feeling anxious are core symptoms in PD patients.

CONCLUSIONS

This study emphasizes the importance of recognizing symptom clusters in PD patients for better symptom management. Five clusters were identified, with key symptoms including itching, decreased interest in sex, fatigue, and anxiety. Early intervention focused on these symptom clusters in PD patients holds promise for alleviating the burden of symptoms.

The State of Open Science Practices in Psychometric Studies of Suicide: A Systematic Review

The adoption of open science practices (OSPs) is crucial for promoting transparency and robustness in research. We conducted a systematic review to assess the frequency and trends of OSPs in psychometric studies focusing on measures of suicidal thoughts and behavior. We analyzed publications from two international databases, examining the use of OSPs such as open access publication, preregistration, provision of open materials, and data sharing. Our findings indicate a lack of adherence to OSPs in psychometric studies of suicide. The majority of manuscripts were published under restricted access, and preregistrations were not utilized. The provision of open materials and data was rare, with limited access to instruments and analysis scripts. Open access versions (preprints/postprints) were scarce. The low adoption of OSPs in psychometric studies of suicide calls for urgent action. Embracing a culture of open science will enhance transparency, reproducibility, and the impact of research in suicide prevention efforts.

Rotation Local Solutions in Multidimensional Item Response Theory Models

We conducted an extensive Monte Carlo study of factor-rotation local solutions (LS) in multidimensional, two-parameter logistic (M2PL) item response models. In this study, we simulated more than 19,200 data sets that were drawn from 96 model conditions and performed more than 7.6 million rotations to examine the influence of (a) slope parameter sizes, (b) number of indicators per factor (trait), (c) probabilities of cross-loadings, (d) factor correlation sizes, (e) model approximation error, and (f) sample sizes on the local solution rates of the oblimin and (oblique) geomin rotation algorithms. To accommodate these design variables, we extended the standard M2PL model to include correlated major factors and uncorrelated minor factors (to represent model error). Our results showed that both rotation methods converged to LS under some conditions with geomin producing the highest local solution rates across many models. Our results also showed that, for identical item response patterns, rotation LS can produce different latent trait estimates with different levels of measurement precision (as indexed by the conditional standard error of measurement). Follow-up analyses revealed that when rotation algorithms converged to multiple solutions, quantitative indices of structural fit, such as numerical measures of simple structure, will often misidentify the rotation that is closest in mean-squared error to the factor pattern (or item-slope pattern) of the data-generating model.

How to evaluate local fit (residuals) in large structural equation models

Consistent with reporting standards for structural equation modelling (SEM), model fit should be evaluated at two different levels, global and local. Global fit concerns the overall or average correspondence between the entire data matrix and the model, given the parameter estimates for the model. Local fit is evaluated at the level of the residuals, or differences between observed and predicted associations for every pair of measured variables in the model. It can happen that models with apparently satisfactory global fit can nevertheless have problematic local fit. This may be especially true for relatively large models with many variables, where serious misspecification is indicated by some larger residuals, but their contribution to global fit is diluted when averaged together with all the other smaller residuals. It can be challenging to evaluate local fit in large models with dozens or even hundreds of variables and corresponding residuals. Thus, the main goal of this tutorial is to offer suggestions about how to efficiently evaluate and describe local fit for large structural equation models. An empirical example is described where all data, syntax and output files are freely available to readers.

The effects of digital nature and actual nature on stress reduction: A meta-analysis and systematic review

Objectives

The study aims to conduct a systematic literature review and meta-analysis to assess the effects of digital nature and actual nature on stress reduction.

Methods

In August 2023, Web of Science, Scopus, ProQuest, PubMed, and EBSCOhost databases were used, and ten articles were in the analysis, with a total sample size of 886 participants. Studies within- or between-subjects design conducted in either a randomized controlled trial or a quasi-experimental design were included. No restriction was put on the year of publication or geographical region. Conference papers and dissertations were also included whereas, book chapters were excluded. Participants included those who were exposed to at least one form of digital nature exposure, such as static images, videos, 360° pictures, and 360° videos. The risk of bias determined through Review Manager 5.4 was used to assess the quality of the studies. STATA software package version 16 was used for visual analysis of funnel plots. For the assessment of potential publication bias, Egger's test was implemented.

Results

Digital natural environments had the same level of stress recovery compared to actual environmental exposures with the same intervention content (SMD = -0.01; 95% CI: -0.15, 0.12). Subgroup analyses and meta-regression indicated that subjective or physiological stress measures, level of immersion, and data extraction method were not associated with pooled effect stress recovery. All subgroups showed comparable stress levels in both conditions. In addition, all included studies had different levels of risk of bias (low, moderate, and high).

Conclusions

The present study concludes that previous research has generally shown that stress levels are reduced in both digital and actual natural environments. The results of the meta-analysis support this conclusion with no significant differences between the two modes of stress recovery through nature viewing.

Methodological quality of systematic reviews on atopic dermatitis treatments: a cross-sectional study

BACKGROUND

Systematic reviews (SRs) could offer the best evidence supporting interventions, but methodological flaws limit their trustworthiness in decision-making. This cross-sectional study appraised the methodological quality of SRs on atopic dermatitis (AD) treatments.

METHODS

We searched MEDLINE, EMBASE, PsycINFO, and Cochrane Database for SRs on AD treatments published in 2019-2022. We extracted SRs' bibliographical data and appraised SRs' methodological quality with AMSTAR (A MeaSurement Tool to Assess systematic Reviews) 2. We explored associations between methodological quality and bibliographical characteristics.

RESULTS

Among the 52 appraised SRs, only one (1.9%) had high methodological quality, while 45 (86.5%) critically low. For critical domains, only five (9.6%) employed comprehensive search strategy, seven (13.5%) provided list of excluded studies, 17 (32.7%) considered risk of bias in primary studies, 21 (40.4%) contained registered protocol, and 24 (46.2%) investigated publication bias. Cochrane reviews, SR updates, SRs with European corresponding authors, and SRs funded by European institutions had better overall quality. Impact factor and author number positively associated with overall quality.

CONCLUSIONS

Methodological quality of SRs on AD treatments is unsatisfactory. Future reviewers should improve the above critical methodological aspects. Resources should be devolved into upscaling evidence synthesis infrastructure and improving critical appraisal skills of evidence users.

Exploring Science Identity and Latent Factors of Student Gains in a Place-based Marine Science CURE Designed to Provide Access to Hawai'i Students from Historically Marginalized Ethnicities

Hawai'i students, and in particular Native Hawaiian students, face high rates of attrition and low representation in Science, Technology, Engineering, and Mathematics (STEM) academic majors and careers, but place-based Course-based Undergraduate Research Experiences (CUREs) such as the Research Experiences in Marine Science (REMS) summer program may help to better engage these students with scientific content understanding and skills development. This article assesses latent factors of student gains after participating in the REMS program as they relate to student science identity. Results from an exploratory factor analysis examining the internal structure of an assessment measure delivered during the program suggest strong evidence of four latent factors in student self-reported learning gains: Content Understanding, Scientific Skills, Interest, and Integration. These factors will guide the development and delivery of the REMS survey as it is applied to additional cohorts of students participating in REMS and other, similar programs being developed and implemented in Hawai'i to support Native Hawaiian students. Although there were no significant relationships between these factors and responses to a science identity survey item, additional insights from an alumna of the program highlight how place-based elements in CUREs provide authentic and rigorous research training experiences for students from populations historically marginalized in STEM.

Development and Validation of Technology-Facilitated Sexual Violence Perpetration and Victimization Scales Among Adults

Technology-facilitated sexual violence (TFSV) includes different forms of digital violence, such as online gender-based violence, online gender- and sexuality-based violence, digital sexual harassment, online sexual coercion, and nonconsensual pornography. The aim of this study was to design and validate a measure to assess the perpetration and victimization of each dimension of TFSV. The relationships between the different dimensions and differences by gender and sexual orientation were also analyzed. The participants were a sample of 2,486 adults (69% women) from Spain, aged between 16 and 79 (M = 25.95; DT = 9.809) years. The Technology-Facilitated Sexual Violence Scales were found to be valid and reliable instruments, supporting our recommendation for the use of these scales. Network analysis and solution-based exploratory factor analyses showed that the dimensions of online sexual coercion and nonconsensual pornography clustered together. All the perpetration variables were related to sexism. Finally, cis women and nonheterosexual people reported higher victimization scores overall compared to cis men and heterosexuals, respectively, while cis men reported higher perpetration scores overall than cis women.

A comparison of conventional and resampled personal reliability in detecting careless responding

Detecting careless responding in survey data is important to ensure the credibility of study findings. Of several available detection methods, personal reliability (PR) is one of the best-performing indices. Curran, Journal of Experimental Social Psychology, 66, 4-19, (2016) proposed a resampled version of personal reliability (RPR). Compared to the conventional PR or even-odd consistency, in which just one set of scale halves is used, RPR is based on repeated calculation of PR across several randomly rearranged sets of scale halves. RPR should therefore be less affected than PR by random errors that may occur when a specific set of scale half pairings is used for the PR calculation. In theory, RPR should outperform PR, but it remains unclear whether it in fact does, and under what conditions the potential gain in detection accuracy is the most pronounced. We conducted two studies: a simulation study examined the performance of the conventional PR and RPR in detecting simulated careless responding, and a real data example study analyzed their performance when detecting human-generated careless responding. In both studies, RPR turned out to be a significantly better careless response indicator than PR. The results also revealed that using 25 resamples for the RPR computation is sufficient to obtain the expected gain in detection accuracy over the conventional PR. We therefore recommend using RPR instead of the conventional PR when screening questionnaire data for careless responding.

Psychometric properties of two ADHD rating scales used in children with ADHD and intellectual disability

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) is often present in people with intellectual disability (ID) and autism. However, few ADHD measures have been developed specifically for individuals with these conditions. There is little literature exploring how well ADHD measures are performing at picking up specific symptoms at the item level.

METHODS

Analyses were conducted on data from 122 children aged 7-15 years old with diagnoses of both ADHD and ID enrolled in the Hyperactivity and Special Educational Needs trial. Parents and teachers completed ratings of ADHD symptoms on the Aberrant Behavior Checklist (ABC) hyperactivity subscale and the revised Conners' Rating Scales hyperactivity scale and ADHD index. Cronbach's alpha was used to examine the reliability of these measures. Item response theory explores the performance of individual items. Multiple indicators, multiple causes models were used to test for measurement invariance by ID severity, co-occurring autism traits and child age.

RESULTS

The reliability of parent and teacher reports of ADHD symptoms on the Conners' and ABC was acceptable across the range of ID. Item performance was generally good, and information was provided across the continuum of ADHD traits. Few items on either measure were non-invariant (i.e., item endorsement generally did not differ based on other child characteristics). When non-invariance was found, the effect was small.

CONCLUSIONS

Both the parent-reported and teacher-reported versions of the Conners' hyperactivity scale and ADHD index and the ABC hyperactivity subscale appear to function well in the current sample of children with co-occurring ADHD and ID.

Calling for Equity-focused Quantitative Methodology in Discipline-based Education Research: An Introduction to Latent Class Analysis

Mixture modeling is a latent variable (i.e., a variable that cannot be measured directly) approach to quantitatively represent unobserved subpopulations within an overall population. It includes a range of cross-sectional (such as latent class [LCA] or latent profile analysis) and longitudinal (such as latent transition analysis) analyses and is often referred to as a "person-centered" approach to quantitative data. This research methods paper describes one type of mixture modeling, LCA, and provides examples of how this method can be applied to discipline-based education research in biology and other science, technology, engineering, and math (STEM) disciplines. This paper briefly introduces LCA, explores the affordances LCA provides for equity-focused STEM education research, highlights some of its limitations, and provides suggestions for researchers interested in exploring LCA as a method of analysis. We encourage discipline-based education researchers to consider how statistical analyses may conflict with their equity-minded research agendas while also introducing LCA as a method of leveraging the affordances of quantitative data to pursue research goals aligned with equity, inclusion, access, and justice agendas.

An analytical approach for identifying trend-seasonal components and detecting unexpected behaviour in psychological time-series

The recent advances in technological capabilities have led to a massive production of time-series data and remarkable progress in longitudinal designs and analyses within psychological research. However, implementing time-series analysis can be challenging due to the various characteristics and complexities involved, as well as the need for statistical expertise. This paper introduces a statistical pipeline on time-series analysis for studying the changes in a single process over time at either a population or individual level, both retrospectively and prospectively. This is achieved through systemization and extension of existing modelling and inference techniques. This analytical approach enables practitioners not only to track but also to model and evaluate emerging trends and apparent seasonality. It also allows for the detection of unexpected events, quantifying their deviations from baseline and forecasting future values. Given that other discernible population- and individual-level changes in psychological and behavioural processes have not yet emerged, continued surveillance is warranted. A near real-time monitoring tool of time-series data could guide community psychological responses across multiple ecological levels, making it a valuable resource for field practitioners and psychologists. An empirical study is conducted to illustrate the implementation of the introduced analytical pipeline in practice and to demonstrate its capabilities.

Clustering broiler farmers based on their behavioural differences towards biosecurity to prevent highly pathogenic avian influenza

Highly pathogenic avian influenza (HPAI) is an important zoonotic disease. The study aims to identify farmer behaviour types to inform the design of behaviour change programmes for mitigating the transmission of HPAI. Therefore, the study utilised multivariate statistical analysis for gaining a better understanding of the relationships among farmers' 30 biosecurity behaviours, the risk of HPAI infection, and distinct features of commercial broiler farmers, which is different from using simple and few binary biosecurity measures. Convenience sampling was used to collect data from 303 Taiwan's farmers among which 40 farmers (13.2%) self-reported having had a HPAI outbreak in the study year while 16 farmers (5.3%) self-reported having had a HPAI outbreak in the past two years. Using categorical principal components analysis and a two-stage cluster analysis, four farmer clusters were identified with distinct features: 1)'Reserved' (4.6%) tended to choose 'No idea' for answering specific questions about HPAI; 2)'Secure' (76.3%) had a higher biosecurity status than the other farms; 3) 'Jeopardised' (16.8%) had a lower biosecurity status than the other farms; 4) 'No-response' (2.3%) tended to skip specific questions about HPAI. The biosecurity status of the 'Reserved' and 'No-response' clusters was undetermined, placing these farms at risk of HPAI infection. Compared to the 'Secure' cluster, the 'Jeopardised' cluster exhibited higher odds of self-reported HPAI in the study year (OR: 2.61, 95% CI: 1.22-5.58) and in the past two years (OR: 4.28, 95% CI: 1.39-13.19). Additionally, the 'Jeopardised' cluster showed increased odds of HPAI recurrence (OR: 4.01, 95% CI: 1.41-11.43). Our study demonstrates that inadequate biosecurity practices can elevate the occurrence or recurrence of HPAI outbreaks. The findings underscore the importance of distinguishing between these clusters to accurately assess the risk of HPAI infection across farms. Furthermore, understanding farmers' behaviours can inform the development of strategies aimed at behaviour change among farmers.

Severity of depressive and anxious symptoms and its association with birth outcomes among pregnant women during the COVID-19 pandemic: a prospective case-control study

AIM

Comparing the anxiety and depression severity and their impact on subsequent birth outcomes in pregnant women before and during Omicron wave in Shanghai in 2022.

METHODS

The depression-anxiety symptoms networks were compared between the pregnant women during the outbreak period (outbreak group; n = 783) and a matched control group of pregnant women before the outbreak (pre-outbreak group; n = 783). The impact of baseline mental state on follow-up pregnancy and neonatal outcomes was also explored by logistic regression.

FINDINGS

Levels of depression and anxiety between the two groups were not significant different. Network analysis showed that central symptom "trouble relaxing" and bridge symptom "depressed mood" shared by both groups. Different symptom associations in different periods of the pandemic. Total scores and sub-symptom scores of prenatal depressive and anxious severities increased the odds ratios of maternal and neonatal syndromes. The influence of mental state on gestational and neonatal outcomes differed across different pandemic periods.

CONCLUSION

The Omicron wave did not have a significant negative impact on the depressive and anxious mood in pregnant women. Targeting central and bridge symptoms intervention may be effective in reducing their adverse effects on co-occurring of anxious and depressive mood and birth outcomes.

The Impact of Insufficient Effort Responses on the Order of Category Thresholds in the Polytomous Rasch Model

Insufficient effort responding (IER) refers to a lack of effort when answering survey or questionnaire items. Such items typically offer more than two ordered response categories, with Likert-type scales as the most prominent example. The underlying assumption is that the successive categories reflect increasing levels of the latent variable assessed. This research investigates how IER affects the intended category order of Likert-type scales, focusing on the category thresholds in the polytomous Rasch model. In a simulation study, we examined several IER patterns in datasets generated from the mixture model for IER (MMIER). The key findings were (a) random responding and overusing the non-extreme categories of a five-category scale were each associated with high frequencies of disordered category thresholds; (b) raising the IER rate from 5% to 10% led to a substantial increase in threshold disordering, particularly among easy and difficult items; (c) narrow distances between adjacent categories (0.5 logits) were associated with more frequent disordering, compared with wide distances (1.0 logits). Two real-data examples highlighted the efficiency and utility of the MMIER for detecting latent classes of respondents exhibiting different forms of IER. Under the MMIER, the frequency of disordered thresholds was reduced substantially in both examples. The discussion focuses on the practical implications of using the MMIER in survey research and points to directions for future research.

Two-Method Measurement Planned Missing Data With Purposefully Selected Samples

Research designs that include planned missing data are gaining popularity in applied education research. These methods have traditionally relied on introducing missingness into data collections using the missing completely at random (MCAR) mechanism. This study assesses whether planned missingness can also be implemented when data are instead designed to be purposefully missing based on student performance. A research design with purposefully selected missingness would allow researchers to focus all assessment efforts on a target sample, while still maintaining the statistical power of the full sample. This study introduces the method and demonstrates the performance of the purposeful missingness method within the two-method measurement planned missingness design using a Monte Carlo simulation study. Results demonstrate that the purposeful missingness method can recover parameter estimates in models with as much accuracy as the MCAR method, across multiple conditions.

Impacts of mandibular setback with or without maxillary advancement for class III skeletal correction on sleep-related respiratory parameters: A systematic review and meta-analysis

A systematic review and meta-analysis was conducted to evaluate the impacts of mandibular setback with or without maxillary advancement for class III skeletal correction on respiratory parameters measured by polysomnography (PSG) and to compare these respiratory parameters between these procedures for class III skeletal correction. Six electronic databases were searched up to June 2023. Studies comparing PSG parameters before and after orthognathic surgery for skeletal class III patients were selected for further analysis. The outcomes of interest were apnoea-hypopnea index (AHI), respiratory disturbance index (RDI), the lowest oxygen saturation (lowest SpO2), the average oxygen saturation (mean SpO2), and the 3% oxygen desaturation index (3% ODI). Data extraction, methodological quality assessment, risk of bias assessment, meta-analysis, and subgroup analysis were performed. Sixteen studies with a total of 476 patients who underwent orthognathic surgery for class III skeletal correction were included for meta-analysis. The risk of bias level was moderate for most studies. All PSG parameters before and after orthognathic surgery were not significantly different. The different surgical procedures also did not significantly affect post-operative PSG parameters. 5.8% of patients developed post-operative obstructive sleep apnoea (OSA). Most of them underwent a large distance of mandibular setback. There is a moderate level of evidence that mandibular setback with or without maxillary advancement for class III skeletal correction does not pre-dispose young and healthy patients to obstructive sleep apnoea when evaluated in the short term after surgery. However, post-operatively developed OSA was found in several isolated cases that underwent a large amount of mandibular setback with or without maxillary advancement.

Introducing ART: A new method for testing auditory memory with circular reproduction tasks

Theories of visual working memory have seen significant progress through the use of continuous reproduction tasks. However, these tasks have mainly focused on studying visual features, with limited examples existing in the auditory domain. Therefore, it is unknown to what extent newly developed memory models reflect domain-general limitations or are specific to the visual domain. To address this gap, we developed a novel methodology: the Auditory Reproduction Task (ART). This task utilizes Shepard tones, which create an infinite rising or falling tone illusion by dissecting pitch chroma and height, to create a 1-360° auditory circular space. In Experiment 1, we validated the perceptual circularity and uniformity of this auditory stimulus space. In Experiment 2, we demonstrated that auditory working memory shows similar set size effects to visual working memory-report error increased at a set size of 2 relative to 1, caused by swap errors. In Experiment 3, we tested the validity of ART by correlating reproduction errors with commonly used auditory and visual working memory tasks. Analyses revealed that ART errors were significantly correlated with performance in both auditory and visual working memory tasks, albeit with a stronger correlation observed with auditory working memory. While these experiments have only scratched the surface of the theoretical and computational constraints on auditory working memory, they provide a valuable proof of concept for ART. Further research with ART has the potential to deepen our understanding of auditory working memory, as well as to explore the extent to which existing models are tapping into domain-general constraints.

The brief mind wandering three-factor scale (BMW-3)

In recent years, researchers from different fields have become increasingly interested in measuring individual differences in mind wandering as a psychological trait. Although there are several questionnaires that allow for an assessment of people's perceptions of their mind wandering experiences, they either define mind wandering in a very broad sense or do not sufficiently separate different aspects of mind wandering. Here, we introduce the Brief Mind Wandering Three-Factor Scale (BMW-3), a 12-item questionnaire available in German and English. The BMW-3 conceptualizes mind wandering as task-unrelated thought and measures three dimensions of mind wandering: unintentional mind wandering, intentional mind wandering, and meta-awareness of mind wandering. Based on results from 1038 participants (823 German speakers, 215 English speakers), we found support for the proposed three-factorial structure of mind wandering and for scalar measurement invariance of the German and English versions. All subscales showed good internal consistencies and moderate to high test-retest correlations and thus provide an effective assessment of individual differences in mind wandering. Moreover, the BMW-3 showed good convergent validity when compared to existing retrospective measures of mind wandering and mindfulness and was related to conscientiousness, emotional stability, and openness as well as self-reported attentional control. Lastly, it predicted the propensity for mind wandering inside and outside the lab (as assessed by in-the-moment experience sampling), the frequency of experiencing depressive symptoms, and the use of functional and dysfunctional emotion regulation strategies. All in all, the BMW-3 provides a brief, reliable, and valid assessment of mind wandering for basic and clinical research.

Sex differences outcomes in conduction system pacing for patients with typical left bundle branch block

BACKGROUND

Biventricular pacing (BVP) appears to confer more pronounced advantages in women, yet the impact of conduction system pacing (CSP) remains insufficiently characterized. This investigation seeks to elucidate sex-specific disparities in clinical outcomes among patients with typical left bundle branch block (LBBB) undergoing CSP, with a particular focus on assessing contributory factors.

METHODS

Consecutive patients diagnosed with nonischemic cardiomyopathy, exhibiting left ventricular ejection fraction (LVEF) ≤ 40%, and manifesting typical LBBB as Strauss criteria, underwent CSP. Subsequent longitudinal monitoring assessed improvements in LVEF and the composite endpoint of mortality or heart failure hospitalization (HFH).

RESULTS

Among the included 176 patients, women (n = 84, mean age: 69.5 ± 8.8 years) displayed smaller heart size (LVEDd, 62.0 ± 8.3 mm vs. 64.8 ± 7.9 mm, P = 0.023) and shorter baseline QRSd (163.5 ± 17.7 ms vs. 169.7 ± 15.1 ms; P = 0.013) than men. Of the 171 patients who completed the follow-up, super-response was observed in 120 (70%), with a higher occurrence in women than men (78.3% vs. 62.5%, P = 0.024). The incidence of death or HFH was numerically lower in women (7.1% Vs 13%, Log-rank P = 0.216). Notably, the super-response showed a significant difference in women compared to men at the same electrocardiography and/or echocardiographic parameters value. Mediation analysis between sex and super-response revealed that LVEDd and pQRSd play an intermediary role, with the mediation proportion of 26.07% and 27.98%, respectively.

CONCLUSIONS

Women may derive more benefits from CSP, and pQRSd and LVEDd partly drive this difference.

Association between cognitive functioning, suicidal ideation and suicide attempts in major depressive disorder, bipolar disorder, schizophrenia and related disorders: A systematic review and meta-analysis

INTRODUCTION

Treatable mental disorders, such as psychotic, major depressive disorder (MDD), and bipolar disorder (BD), contribute to a substantial portion of suicide risk, often accompanied by neurocognitive deficits. We report the association between cognitive function and suicidal ideation/suicide attempts (SI/SA) in individuals with schizoaffective disorder, BD, and MDD.

METHODS

A systematic search was conducted on PubMed, Ovid and Scopus databases for primary studies published from inception to April 2024. Eligible articles that reported on the effect size of association between cognition and SI/SA were pooled using a random effects model.

RESULTS

A total of 41 studies were included for analysis. There was a negative association between executive functioning and SI/SA in schizoaffective disorder (SA: Corr = -0·78, 95 % CI [-1·00, 0·98]; SI: Corr = -0·06, 95 % CI [-0·85, 0·82]) and MDD (SA: Corr = -0·227, 95 % CI [-0·419, -0·017]; SI: Corr = -0·14, 95 % CI [-0·33, 0·06]). Results were mixed for BD, with a significant positive association between SA and global executive functioning (Corr = 0·08, 95 % CI [0·01, 0·15]) and negative association with emotion inhibition. Mixed results were observed for processing speed, attention, and learning and memory, transdiagnostically.

LIMITATIONS

There is heterogeneity across sample compositions and cognitive measures. We did not have detailed information on individuals with respect to demographics and comorbidities.

CONCLUSIONS

We observed a transdiagnostic association between measures of cognitive functions and aspects of suicidality. The interplay of cognitive disturbances, particularly in reward-based functioning, may underlie suicidality in individuals with mental disorders. Disturbances in impulse control, planning, and working memory may contribute to self-injurious behavior and suicide.

Perceived stress and suicidal ideation among transgender women in Shenyang and Kunming, China: Exploring the mediating roles of thwarted belongingness, perceived burdensomeness and social exclusion

BACKGROUND

Transgender women have a high prevalence of suicidal ideation, with stress and low interpersonal needs as its main risk factors. This study aimed to investigate the mediating role of interpersonal needs on the relationship between perceived stress and suicidal ideation among transgender women in China.

METHODS

A cross-sectional study was conducted in Shenyang and Kunming from April to September 2018. 247 transgender women were recruited. Sociodemographic statistics, perceived stress, interpersonal needs and suicidal ideation were obtained. Correlation analysis and mediation analysis were performed to test the relationship among perceived stress, suicidal ideation and interpersonal needs.

RESULTS

14.6 % of the participants reported suicidal ideation within a year. Perceived stress was positively correlated with suicidal ideation (r = 0.228, p < 0.001), interpersonal needs and its three dimensions (r = 0.300-0.583, ps < 0.001)-thwarted belongingness, perceived burdensomeness and social exclusion. Interpersonal needs and its three dimensions were also positively correlated with suicidal ideation (r = 0.148-0.299, ps < 0.05). Interpersonal needs, perceived burdensomeness and social exclusion partly mediated the relationship between perceived stress and suicidal ideation, while thwarted belongingness didn't play a mediating role.

LIMITATIONS

Cross-sectional study limited confirmation of causality between variables. The investigation didn't aim at the specific stressors of sexual minorities. And that participants came from only two regions might affect the generalization of the results.

CONCLUSIONS

We found a partial mediating role of interpersonal needs between stress and suicidal ideation. Stress reduction and increased social inclusion are needed to reduce suicidal ideation in transgender women.

Global tropical and extra-tropical tropospheric ozone trends and radiative forcing deduced from satellite and ozonesonde measurements for the period 2005-2020

Tropospheric ozone (TPO) is considered as a "near-term climate forcer", whose impact on climate depends on its radiative forcing (RF), which is a change in the Earth's energy flux. Here, we use the ground-based and satellite measurements during the period 2005-2020 to deduce the trends of TPO, which is significantly positive in the tropical and extra-tropical northern hemisphere (0.2-0.5 DU/yr) and southern extra-tropics (0.1-0.2 DU/yr). Furthermore, the trends derived using a multiple linear regression model (MLR) also confirm these estimates, which are about 0.05-0.1 DU/yr and the regions with higher trends (>0.06 DU/yr) are statistically significant. We also use a standalone Rapid Radiative Transfer Model coupled with a convective model (Radiative-Convective Model; RCM) to assess the climate forcing of ozone using its vertical profiles from the Modern Era Retrospective Reanalysis (MERRA)-2 reanalysis. The estimated temperature rise due to the radiative forcing of ozone in the tropical troposphere (1000-100 hPa) is about 0.2-0.3 °C for the study period. In brief, there is a positive trend in the tropospheric ozone in the tropics and extra-tropics, which is a great concern for regional warming, public health and ecosystem dynamics.

Psychological resilience is positively correlated with Habenula volume

BACKGROUND

Psychological resilience is defined as the process and outcome of individuals' successful adaptation to challenging life experiences. The Habenula (Hb) is known to be involved in the stress response; however, the relationship between Hb volume and resilience in humans remains unclear. This study investigated the correlation among resilience, Hb volume, and depressive tendencies in adults.

METHODS

Hb volumes were assessed using deep learning techniques applied to 110 healthy participants. Resilience and depression were evaluated using the Connor-Davidson Resilience Scale and Beck Depression Inventory-II, respectively. We examined the relationship between Hb volume and resilience and assessed the mediating effects of resilience on the relationship between Hb volume and depressive tendencies.

RESULTS

Correlation analysis revealed a positive correlation between resilience and Hb volume (partial r = 0.176, p = 0.001), which was more pronounced in women (partial r = 0.353, p = 0.003). Hb volumes on the left and right sides exhibited significant lateralization (LI = 0.031, 95 % CI = [0.016, 0.046]). Despite Hb asymmetry, lateralization was not significantly associated with resilience. The mediation analysis shows significant indirect effect of resilience on the relationship between Hb volume and depressive tendencies (β = -0.093, 95%CI = [-0.189, -0.019]).

CONCLUSION

This study found that populations with lower resilience have smaller Hb volume. Previous research has shown that Hb volume decreased with the increasing severity of depression symptoms in patients. Our findings support this view and extend it to a population that has not been clinically diagnosed with depression. Additionally, we found that psychological resilience can be predicted by Hb volume and may serve as a mediating factor indirectly affecting depressive tendencies, even in healthy individuals.

LIMITATIONS

Due to its cross-sectional design, this study was unable to analyze dynamic changes in Hb volume during the process of resilience adaptation.

Prevention of Heart Failure in Hypertension-the Role of Coronary Heart Disease Events Treated With Versus Without Revascularization: The ALLHAT Study

In modern clinical practice, less than half of patients with new-onset heart failure (HF) undergo ischemic evaluation and only a minority undergo revascularization. We aimed to assess the proportion of the effect of hypertension (antihypertensive treatment) on incident HF to be eliminated by prevention of coronary heart disease (CHD) event treated with or without revascularization, considering possible treatment-mediator interaction. The causal mediation analysis of Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT) included 42,418 participants (age 66.9 ± 7.7, 35.6% black, 53.2% men). A new CHD event (myocardial infarction or angina) that occurred after randomization but before the incident HF outcome was the mediator. Incident symptomatic congestive HF (CHF) and hospitalized/fatal HF (HHF) were the primary and secondary outcomes, respectively. Logistic regression (for mediator) and Cox proportional hazards regression (for outcome) were adjusted for demographics, cardiovascular disease history, and risk factors. During a median 4.5-year follow-up, 2,785 patients developed CHF, including 2,216 HHF events. Participants who developed CHD events had twice the higher incidence rate of CHF than CHD-free (28.5 vs 13.9 events/1,000 person-years). The proportion of reference interaction indicating direct harm because of a CHD event for lisinopril (234% for CHF, 355% for HHF) and amlodipine (244% for CHF, 468% for HHF) was greater than for chlortalidone (143% for CHF, 269% for HHF). In patients with revascularized CHD events, chlortalidone and amlodipine eliminated 21% to 24% and lisinopril eliminated -45% of HHF. Antihypertensive treatment could not eliminate harm from CHD events treated without revascularization. In conclusion, the antihypertensive drugs (chlortalidone, lisinopril, and amlodipine) prevent HF not principally by preventing CHD events but by way of other pathways. HF is moderated but not mediated by CHD events. Revascularization of CHD events is paramount for HF prevention.

Analysis of wastewater treatment plant data identifies the drivers of PFAS enrichment in foams

The concept of incorporating foam fractionation in aerated bioreactors at wastewater treatment plants (WWTPs) for the removal of per- and polyfluoroalkyl substances (PFAS) has recently been proposed. The extent of PFAS enrichment in aerated bioreactors' foams, as indicated by enrichment factors (EFs), has been observed to vary widely. Laboratory evidence has shown that factors affecting PFAS enrichment in foams include conductivity, surfactant concentrations and initial PFAS concentrations. However, real wastewaters are complex heterogenous matrices with physical, chemical and biological characteristics potentially contributing to the phenomenon of PFAS partitioning into foams. In this study, we characterised mixed liquor suspensions, including conductivity, filament content, aqueous PFAS concentrations, surface tension and total suspended solids concentrations (TSS) as well as foams, including bubble size and half-life. We used statistical tools - linear mixed-effects model - to establish relationships between PFAS enrichment in aerated bioreactor foams and the examined characteristics. We found that some of the examined characteristics, specifically filament content, surface tension and TSS concentrations measured in mixed liquor suspension and foam half-life, are negatively and significantly associated with the enrichment of longer chain PFAS (with perfluorinated carbon number ≥ 6). Of these, filament content is the important determinant of PFAS enrichment, potentially leading to an increase in, for example, perfluorooctanoic acid (PFOA) EF from 3 to 100 between typical filamentous and non-filamentous suspended biomass. However, enrichment of shorter chain PFAS (with perfluorinated carbon number ≤ 5) is negligible and is not affected by the characteristics that were measured. The findings of our study may serve as valuable information for the implementation of foam fractionation at WWTPs by elucidating the drivers that contribute to the enrichment of longer chain PFAS, under conditions typically found at WWTPs.