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We develop a generalized method of moments (GMM) approach for fast parameter estimation in a new class of Dirichlet latent variable models with mixed data types. Parameter estimation via GMM has computational and statistical advantages over alternative methods, such as expectation maximization, variational inference, and Markov chain Monte Carlo. A key computational advantage of our method, Moment Estimation for latent Dirichlet models (MELD), is that parameter estimation does not require instantiation of the latent variables. Moreover, performance is agnostic to distributional assumptions of the observations. We derive population moment conditions after marginalizing out the sample-specific Dirichlet latent variables. The moment conditions only depend on component mean parameters. We illustrate the utility of our approach on simulated data, comparing results from MELD to alternative methods, and we show the promise of our approach through the application to several datasets. Supplementary materials for this article are available online.

A new method is proposed to estimate graphical models simultaneously from data obtained at different coarseness scales. Starting from a predefined scale the method offers the possibility to zoom in or out over scales on particular edges. The estimated graphs over the different scales have similar structures although their level of sparsity depends on the scale at which estimation takes place. The method makes it possible to evaluate the evolution of the graphs from the coarsest to the finest scale or vice versa. We select an optimal coarseness scale to be used for further analysis. Simulation studies and an application on functional magnetic resonance brain imaging data show the method's performance in practice.

Modelling and computation for multivariate longitudinal surveys have proven challenging, particularly when data are not all continuous and Gaussian but contain discrete measurements. In many social science surveys, study participants are selected via complex survey designs such as stratified random sampling, leading to discrepancies between the sample and population, which are further compounded by missing data and loss to follow-up. Survey weights are typically constructed to address these issues, but it is not clear how to include them in models. Motivated by data on sexual development, we propose a novel non-parametric approach for mixed scale longitudinal data in surveys. In the approach proposed, the mixed scale multivariate response is expressed through an underlying continuous variable with dynamic latent factors inducing time varying associations. Bias from the survey design is adjusted for in posterior computation relying on a Markov chain Monte Carlo algorithm. The approach is assessed in simulation studies and applied to the National Longitudinal Study of Adolescent to Adult Health.

Pearson residuals aid the task of identifying model misspecification because they compare the estimated, using data, model with the model assumed under the null hypothesis. We present different formulations of the Pearson residual system that account for the measurement scale of the data and study their properties. We further concentrate on the case of mixed-scale data, that is, data measured in both categorical and interval scale. We study the asymptotic properties and the robustness of minimum disparity estimators obtained in the case of mixed-scale data and exemplify the performance of the methods via simulation.

The category utility function is a partition quality scoring function applied in some clustering programs of machine learning. We reinterpret this function in terms of the data variance explained by a clustering, or, equivalently, in terms of the square-error classical clustering criterion that administers the K-Means and Ward methods. This analysis suggests extensions of the scoring function to situations with differently standardized and mixed scale data.[PUBLICATION ABSTRACT]

Pathways to diagnosis in adulthood are poorly understood. Even less is known about undiagnosed adults who believe they may be autistic. This mixed-methods online survey examined adults’ journeys from initial concern to receiving the diagnosis. Quantitative findings showed the diagnostic process to be highly heterogeneous. Qualitative analysis identified desires for explanation and support as motives for seeking diagnosis. Cost and fear of not being taken seriously were major barriers, echoed by qualitative responses that described the process as confusing, expensive and time-consuming. While most participants were satisfied with the diagnosis, their emotional reactions were complex. Findings support the need for thoroughly implementing national guidelines, and for improved knowledge and communication in mainstream clinicians encountering clients with possible autism characteristics.

PurposeOrganizations use Industry 4.0 readiness models to evaluate their preparedness prior to the implementation of Industry 4.0. Though there are many studies on Industry 4.0 readiness models, the dimensions of readiness differ. Besides, there is no study empirically validating the readiness model in different sectors or types of organization. The purpose of this study is to conceptualize the dimensions of the Industry 4.0 readiness model and subsequently evaluate the criticality of these dimensions in manufacturing, service, small and medium-sized enterprises (SMEs) and large enterprises (LEs).Design/methodology/approachThe study uses an exploratory sequential mixed method design. In phase one, 37 senior managers participated through a purposive sampling frame. In phase two, 70 senior managers participated in an online survey.FindingsThe results of the study indicated that the Industry 4.0 readiness model has 10 dimensions. Further, the criticality of the dimensions as applied to different sectors and type of organizations is put forward. This study will help manufacturing, services, SMEs and LEs to evaluate Industry 4.0 readiness before commencing the deployment of Industry 4.0.Practical implicationsThe findings can be very beneficial for Industry 4.0 practitioners and senior managers in different organisations to understand what readiness dimensions need to be considered prior to implementation of Industry 4.0 technology.Originality/valueThis paper makes an attempt to conceptualize the Industry 4.0 readiness model and utilizes an exploratory mixed method for critically evaluating the dimensions related to the model.

A general method for constructing low‐rank tensor product smooths for use as components of generalized additive models or generalized additive mixed models is presented. A penalized regression approach is adopted in which tensor product smooths of several variables are constructed from smooths of each variable separately, these “marginal” smooths being represented using a low‐rank basis with an associated quadratic wiggliness penalty. The smooths offer several advantages: (i) they have one wiggliness penalty per covariate and are hence invariant to linear rescaling of covariates, making them useful when there is no “natural” way to scale covariates relative to each other; (ii) they have a useful tuneable range of smoothness, unlike single‐penalty tensor product smooths that are scale invariant; (iii) the relatively low rank of the smooths means that they are computationally efficient; (iv) the penalties on the smooths are easily interpretable in terms of function shape; (v) the smooths can be generated completely automatically from any marginal smoothing bases and associated quadratic penalties, giving the modeler considerable flexibility to choose the basis penalty combination most appropriate to each modeling task; and (vi) the smooths can easily be written as components of a standard linear or generalized linear mixed model, allowing them to be used as components of the rich family of such models implemented in standard software, and to take advantage of the efficient and stable computational methods that have been developed for such models. A small simulation study shows that the methods can compare favorably with recently developed smoothing spline ANOVA methods.

Objective The aim of this study was to investigate the perceptions of health profession students regarding ChatGPT use and the potential impact of integrating ChatGPT in healthcare and education. Background Artificial Intelligence is increasingly utilized in medical education and clinical profession training. However, since its introduction, ChatGPT remains relatively unexplored in terms of health profession students' acceptance of its use in education and practice. Design This study employed a mixed-methods approach, using a web-based survey. Methods The study involved a convenience sample recruited through various methods, including Faculty of Medicine announcements, social media, and snowball sampling, during the second semester (March to June 2023) . Data were collected using a structured questionnaire with closed-ended questions and three open-ended questions. The final sample comprised 217 undergraduate health profession students, including 73 (33.6%) nursing students, 65 (30.0%) medical students, and 79 (36.4%) occupational therapy, physiotherapy, and speech therapy students. Results Among the surveyed students, 86.2% were familiar with ChatGPT, with generally positive perceptions as reflected by a mean score of 4.04 (SD = 0.62) on a scale of 1 to 5. Positive feedback was particularly noted with respect to ChatGPT's role in information retrieval and summarization. The qualitative data revealed three main themes: experiences with ChatGPT, its impact on the quality of healthcare, and its integration into the curriculum. The findings highlight benefits such as serving as a convenient tool for accessing information, reducing human errors, and fostering innovative learning approaches. However, they also underscore areas of concern, including ethical considerations, challenges in fostering critical thinking, and issues related to verification. The absence of significant differences between the different fields of study indicates consistent perceptions across nursing, medicine, and other health profession students. Conclusions Our findings underscore the necessity for continuous refinement to enhance ChatGPT's accuracy, reliability, and alignment with the diverse educational needs of health professions. These insights not only deepen our understanding of student perceptions of ChatGPT in healthcare education but also have significant implications for the future integration of AI in health profession practice. The study emphasizes the importance of a careful balance between leveraging the benefits of AI tools and addressing ethical and pedagogical concerns.

Emergency departments worldwide are faced with in-hospital crowding and fast-paced admissions, creating an increasingly high workload for health care personnel. In recent years, emergency departments have also experienced an increase in emergency admissions, resulting in burdened workplaces. This has led to debates about nurses’ work environment and mental well-being. This study aimed to gain knowledge on the prevalence of depression, anxiety, and stress, as well as insight into the factors influencing the mental well-being of the nursing staff in a Danish emergency department. This is a mixed-methods study with an explanatory sequential design. A questionnaire (the Depression, Anxiety, and Stress Scale – 21 Items) was sent to nursing staff (N = 146) in a large emergency department in the Region of Southern Denmark. Afterward, a smaller sample participated in semistructured interviews. The quantitative data were analyzed using descriptive statistics, the Mann–Whitney U test, and the chi-square test. In the qualitative part, a thematic analysis was performed. Completed surveys were received from 78 nursing staff (53.4%). Overall, the nursing staff reported severe to extremely severe levels of depression (14.1%), anxiety (23.1%), or stress (47.2%) within a week before completing the survey. Higher levels of psychological distress were significantly associated with fewer years of clinical experience and having previously experienced or received treatment for depression, anxiety, or stress. Ten staff members later volunteered to participate in the interviews. The qualitative results formed 3 themes: (1) high work pace and responsibility, (2) professional community and nursing identity, and (3) culture with an increased focus on mental well-being. The nursing staff reported high mental strain, especially in the forms of high stress and anxiety levels. They explained that their mental health was affected by overcrowding, a pressured work environment, and lack of resources.