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The main purpose of this study is to improve estimation efficiency in obtaining maximum marginal likelihood estimates of contextual effects in the framework of nonlinear multilevel latent variable model by adopting the Metropolis-Hastings Robbins—Monro algorithm (MH-RM). Results indicate that the MH-RM algorithm can produce estimates and standard errors efficiently. Simulations, with various sampling and measurement structure conditions, were conducted to obtain information about the performance of nonlinear multilevel latent variable modeling compared to traditional hierarchical linear modeling. Results suggest that nonlinear multilevel latent variable modeling can more properly estimate and detect contextual effects than the traditional approach. As an empirical illustration, data from the Programme for International Student Assessment were analyzed.

Background Burnout is an increasing public health concern that afflicts employees globally. The measurement of burnout is not without criticism, specifically in the context of its operational definition as a syndrome, also recently designated as such by the World Health Organisation. The Burnout Assessment Tool (BAT-23) is a new measure for burnout that addresses many of the criticisms surrounding burnout scales. The aim of this study is to determine the validity, reliability, and measurement invariance of the BAT-23 in South Africa. Method A quantitative, cross-sectional survey, approach was taken ( n  = 1048). Latent variable modelling was implemented to investigate the construct-relevant multidimensionality that is present in the BAT. For measurement invariance, the configural, metric, scalar, and strict models were tested. Results The analyses showed that the hierarchical operationalisation of BAT-assessed burnout was the most appropriate model for the data. Specifically, a bifactor ESEM solution. Composite reliability estimates were all well above the cut-off criteria for both the global burnout factor and the specific factors. The measurement invariance tests showed that gender achieved not only strong invariance, but also strict invariance. However, ethnicity initially only showed strong invariance, but a test of partial strict invariance did show that the mean scores could be fairly compared between the groups when releasing certain constraints. Conclusions The BAT-23 is a valid and reliable measure to investigate burnout within the Southern African context.

The use of automated sensors has grown rapidly in recent years, with sensor data now routinely used for monitoring in a wide range of situations, including human health and behaviour, the environment, wildlife, and agriculture. Livestock farming is a key area of application, and our primary focus here, but the issues discussed are widely applicable. There is the potential to massively increase the use of empirical data for decision-making in real time, and a range of quantitative methods, including machine learning and statistical methods, have been proposed for this purpose within the literature. In many areas, however, development and validation of quantitative approaches are still needed in order for these methods to effectively inform decision-making. Within the context of livestock farming, for example, it must be practically feasible to repeatedly apply the method dynamically in real time on farms in order to optimise decision-making, and we discuss the challenges in using quantitative approaches for this purpose. It is also crucial to evaluate and compare the applied performance of methods in a fair and robust way—such comparisons are currently lacking within the literature on livestock farming, and we outline approaches to addressing this key gap.

This paper introduces a new latent variable probabilistic framework for representing spectral data of high spatial and spectral dimensionality, such as hyperspectral images. We use a generative Bayesian model to represent the image formation process and provide interpretable and efficient inference and learning methods. Surprisingly, our approach can be implemented with simple tools and does not require extensive training data, detailed pixel-by-pixel labeling, or significant computational resources. Numerous experiments with simulated data and real benchmark scenarios show encouraging image classification performance. These results validate the unique ability of our framework to discriminate complex hyperspectral images, irrespective of the presence of highly discriminative spectral signatures.

This study presents a geometry-aware generative modelling framework for earthquake response spectra, leveraging a hierarchical variational autoencoder (HVAE) with latent variables embedded in a Poincaré ball manifold. Predicting complete ground motion response spectra is crucial for seismic hazard analysis and structural performance assessment; however, conventional machine learning models often fail to capture multi-scale physical dependencies and hierarchical uncertainity inherent in earthquake records that arise from event-to-event variablity and spatial variability. The proposed architecture is trained using source and site parameters to regularise the latent space which enables the generation of physically consistent spectral amplitudes while explicitly modelling inter- and intra-event variabilities . By exploiting hyperbolic latent geometry, the HVAE encodes hierarchical relationships into the latent space with an improved representational efficiency. Trained on a curated strong-motion dataset, the model achieves high reconstruction fidelity, with a mean coefficient of determination of 0.961 across all spectral periods. Integration into stochastic ground motion simulation and early warning pipelines demonstrates its practical utility. This work bridges geometric deep learning and seismological modelling, offering a principled, domain-aligned approach to real-time seismic risk mitigation.

Neighbourhood variation in socioeconomic deprivation is recognised as a small but meaningful determinant of adolescent mental health, yet the mechanisms through which the effects operate remain poorly understood. This study used #BeeWell survey data collected from adolescents in Greater Manchester (England) in 2021–2023 (life satisfaction: N = 27,009; emotional difficulties: N = 26,461). Through Latent Growth Mixture Modelling, we identified four non-linear trajectories of life satisfaction (Consistently High (71.0%), Improving (8.7%), Deteriorating (6.3%), and Consistently Low (13.9%); entropy = 0.66) and three non-linear trajectories of emotional difficulties (Low/Lessening (53.7%), Sub-Clinical (38.3%), and Elevated/Worsening (8.0%); entropy = 0.61). Using a multi-level mediation framework we assessed (1) whether neighbourhood deprivation predicted trajectory class membership and (2) the extent to which effects of deprivation operate through aspects of Community Wellbeing, as measured by the Co-op Community Wellbeing Index (CWI). Greater deprivation increased the odds of following Deteriorating (OR = 1.081, [1.023, 1.12]) and Consistently Low (OR = 1.084, [1.051, 1.119]) life satisfaction trajectories and reduced the odds of following a Sub-Clinical emotional difficulties trajectory (OR = 0.975, [0.954, 0.996]). Mediation analyses revealed that the effects of deprivation on Consistently Low life satisfaction partially operate through Equality (ab = 0.016, [0.002, 0.029]) and Housing, Space, and Environment (ab = −0.026, [−0.046, −0.006]). Further indirect effects were observed for Housing, Space, and Environment, which reduced likelihood of Sub-Clinical emotional difficulties for those living in deprived neighbourhoods (ab = −0.026, [−0.045, −0.008]). The findings highlight the distinct effects of neighbourhood deprivation on affective and evaluative domains of adolescent mental health and the protective effect of housing and related environmental factors in disadvantaged contexts, advancing our understanding of the mechanisms underpinning neighbourhood effects on dual-state adolescent mental health.

PurposeThe COVID-19 pandemic has affected the way many individuals go about their daily lives. This study attempted to model the complexity of change in lifestyle quality as a result of the COVID-19 pandemic and its context within the UK adult population.MethodsData from the COVID-19 Psychological Research Consortium Study (Wave 3, July 2020; N = 1166) were utilised. A measure of COVID-19-related lifestyle change captured how individuals’ lifestyle quality had been altered as a consequence of the pandemic. Exploratory factor analysis and latent profile analysis were used to identify distinct lifestyle quality change subgroups, while multinomial logistic regression analysis was employed to describe class membership.ResultsFive lifestyle dimensions, reflecting partner relationships, health, family and friend relations, personal and social activities, and work life, were identified by the EFA, and seven classes characterised by distinct patterns of change across these dimensions emerged from the LPA: (1) better overall (3.3%), (2) worse except partner relations (6.0%), (3) worse overall (2.5%), (4) better relationships (9.5%), (5) better except partner relations (4.3%), (6) no different (67.9%), and (7) worse partner relations only (6.5%). Predictor variables differentiated membership of classes. Notably, classes 3 and 7 were associated with poorer mental health (COVID-19 related PTSD and suicidal ideation).ConclusionsFour months into the pandemic, most individuals’ lifestyle quality remained largely unaffected by the crisis. Concerningly however, a substantial minority (15%) experienced worsened lifestyles compared to before the pandemic. In particular, a pronounced deterioration in partner relations seemed to constitute the more severe pandemic-related lifestyle change.

The COVID-19 pandemic has disproportionately affected women's mental health. However, most evidence has focused on mental illbeing outcomes, and there is little evidence on the mechanisms underlying this unequal impact. To investigate gender differences in the long-term trajectories of life satisfaction, how these were affected during the pandemic and the role of time-use differences in explaining gender inequalities. We used data from 6766 (56.2% women) members of the 1970 British Cohort Study (BCS70). Life satisfaction was prospectively assessed between the ages of 26 (1996) and 51 (2021) years, using a single question with responses ranging from 0 (lowest) to 10 (highest). We analysed life satisfaction trajectories with piecewise latent growth curve models, and investigated whether gender differences in the change in the life satisfaction trajectories with the pandemic were explained by self-reported time spent doing different paid and unpaid activities. Women had consistently higher life satisfaction than men before the pandemic (Δ = 0.213, 95% CI 0.087-0.340; 0.001) and experienced a more accelerated decline with the pandemic onset (Δ = -0.018, 95% CI -0.026 to -0.011; < 0.001). Time-use differences did not account for the more accelerated decrease in women's life satisfaction levels with the pandemic (Δ = -0.016, 95% CI -0.031 to -0.001; = 0.035). Our study shows pronounced gender inequalities in the impact of the pandemic on the long-term life satisfaction trajectories of adults in their 50s, with women losing their pre-pandemic advantage over men. Self-reported time-use differences did not account for these inequalities. More research is needed to tackle gender inequalities in population mental health.

Cognitive affective biases describe the tendency to process negative information or positive information over the other. These biases can be modulated by changing extracellular serotonin (5‐HT) levels in the brain, for example, by pharmacologically blocking and downregulating the 5‐HT transporter (5‐HTT), which remediates negative affective bias. This suggests that higher levels of 5‐HTT are linked to a priority of negative information over positive, but this link remains to be tested in vivo in healthy individuals. We, therefore, evaluated the association between 5‐HTT levels, as measured with [11C]DASB positron emission tomography (PET), and affective biases, hypothesising that higher 5‐HTT levels are associated with a more negative bias. We included 98 healthy individuals with measures of [11C]DASB binding potential (BPND) and affective biases using The Emotional Faces Identification Task by subtracting the per cent hit rate for happy from that of sad faces (EFITAB). We evaluated the association between [11C]DASB BPND and EFITAB in a linear latent variable model, with the latent variable (5‐HTTLV) modelled from [11C]DASB BPND in the fronto‐striatal and fronto‐limbic networks implicated in affective cognition. We observed an inverse association between 5‐HTTLV and EFITAB (β = −8% EFITAB per unit 5‐HTTLV, CI = −14% to −3%, p = .002). These findings show that higher 5‐HTT levels are linked to a more negative bias in healthy individuals. High 5‐HTT supposedly leads to high clearance of 5‐HT, and thus, a negative bias could result from low extracellular 5‐HT. Future studies must reveal if a similar inverse association exists in individuals with affective disorders. In a sample of 98 healthy individuals, we found that the serotonin transporter, as measured with [11C]DASB positron emission tomography, is inversely associated with cognitive affective biases so that higher levels of the serotonin transporter are associated with a more negative affective bias in cognitive processing.

In memory of Dr. Dennis John McFarland, who passed away recently, our objective is to continue his efforts to compare psychometric networks and latent variable models statistically. We do so by providing a commentary on his latest work, which he encouraged us to write, shortly before his death. We first discuss the statistical procedure McFarland used, which involved structural equation modeling (SEM) in standard SEM software. Next, we evaluate the penta-factor model of intelligence. We conclude that (1) standard SEM software is not suitable for the comparison of psychometric networks with latent variable models, and (2) the penta-factor model of intelligence is only of limited value, as it is nonidentified. We conclude with a reanalysis of the Wechlser Adult Intelligence Scale data McFarland discussed and illustrate how network and latent variable models can be compared using the recently developed R package Psychonetrics. Of substantive theoretical interest, the results support a network interpretation of general intelligence. A novel empirical finding is that networks of intelligence replicate over standardization samples.