How Social Care Services can be designed using cause-effect models and Bayesian analysis. A study in Scotland

IntroductionIn Vietnam, cardiovascular diseases (CVDs) are serious health issues, especially in the context of overload central heart hospitals, insufficient primary healthcare, and lack of customer-oriented care and treatment. Attempts to measure demand and willingness-to-pay (WTP) for different CVD treatments and care services have been limited. This study explored the preferences and WTP of patients with heart diseases for different home- and hospital-based services in Hanoi, Vietnam.MethodsA cross-sectional survey was performed at the Hanoi Heart Hospital from July to December 2017. A contingent valuation was adopted to determine the preferences of patients and measure their WTP. Interval regressions were employed to determine the potential predictors of patients’ WTP.ResultsHospital-based services were most preferred by patients, with demand ranging from 45.6% to 82.3% of total participants, followed by home-based (45.4%–45.8%) and administrative services (28.9%–34%). WTP for hospital-based services were in the range of US$ 9.8 (US$ 8.4–11.2)–US$ 21.9 (US$ 20.3–23.4), while figures for home-based and administrative services were US$ 9.8 (US$ 8.4–11.2)–US$ 22 (US$ 18.7–25.3) and 1.9 (US$ 1.6–2.2)–US$ 7.5 (US$ 6.3–8.6), respectively. Patients who lived in urban areas, were employed, were having higher level of education, and were not covered by health insurance were willing to pay more for services, especially home-based ones.ConclusionDemand and WTP for home-based services among heart disease patients were moderately low compared with hospital-based ones. There is a need for more policies supporting home-based services, better communication of services’ benefits to general public and patients, and introduction of services packages based on patients’ preferences.

A global sensitivity analysis and Bayesian inference framework was developed for improving the parameterization and predictability of a monthly time step process‐based biogeochemistry model. Using a Latin Hypercube sampler and an existing Terrestrial Ecosystem Model (TEM), a set of 500,000 Monte Carlo ensemble simulations was conducted for a black spruce forest ecosystem. A global sensitivity analysis was then conducted to identify the key model parameters and examine the interaction structures among TEM parameters. Bayesian inference analysis was also performed using these ensemble simulations and eddy flux data of carbon, latent heat flux, and MODIS gross primary production (GPP) to reduce the uncertainty of parameter estimation and prediction of TEM. We found that (1) the simulated carbon fluxes are mostly affected by parameters of the maximum rate of photosynthesis (CMAX), the half‐saturation constant for CO2 uptake by plants (kc), the half‐saturation constant for Photosynthetically Active Radiation used by plants (ki), and the change in autotrophic respiration due to 10°C temperature increase (RHQ10); (2) the effect of parameters on seasonal carbon dynamics varies from one parameter to another during a year; (3) to well constrain the uncertainties of TEM predictions and parameters using the Bayesian inference technique, at least two different fluxes of NEP, GPP, and ecosystem respiration (RESP) are required; and (4) different assumptions of the error structures of the flux data used in the Bayesian inference analysis result in different uncertainty bounds of the posterior parameters and model predictions. We further found that, using the Bayesian framework and eddy flux and satellite data, the uncertainty of simulated carbon fluxes has been remarkably reduced. The developed global sensitivity analysis and Bayesian framework could further be used to analyze and improve the predictability and parameterization of relatively coarse time step biogeochemistry models when the eddy flux and satellite data are available for other terrestrial ecosystems.

Process monitoring plays an important role in ensuring the safety and stable operation of equipment in a large-scale process. This paper proposes a novel data-driven process monitoring framework, termed the ensemble adaptive sparse Bayesian transfer learning machine (EAdspB-TLM), for nonlinear fault diagnosis. The proposed framework has the following advantages: Firstly, the probabilistic relevance vector machine (PrRVM) under Bayesian framework is re-derived so that it can be used to forecast the plant operating conditions. Secondly, we extend the PrRVM method and assimilate transfer learning into the sparse Bayesian learning framework to provide it with the transferring ability. Thirdly, the source domain (SD) data are re-enabled to alleviate the issue of insufficient training data. Finally, the proposed EAdspB-TLM framework was effectively applied to monitor a real wastewater treatment process (WWTP) and a Tennessee Eastman chemical process (TECP). The results further demonstrate that the proposed method is feasible.

This paper reports the estimation of the unknown boundary heat flux from a fin using the Bayesian inference method. The setup consists of a rectangular mild steel fin of dimensions 250×150×6 mm3 and an aluminium base plate of dimensions 250×150×8 mm3. The fin is subjected to constant heat flux at the base and the fin setup is modelled using ANSYS14.5. The problem considered is a conjugate heat transfer from the fin, and the Navier–Stokes equation is solved to obtain the flow parameters. Grid independence study is carried out to fix the number of grids for the study considered. To reduce the computational cost, computational fluid dynamics (CFD) is replaced with artificial neural network (ANN) as the forward model. The Markov Chain Monte Carlo (MCMC) powered by Metropolis–Hastings sampling algorithm along with the Bayesian framework is used to explore the estimation space. The sensitivity analysis of the estimated temperature with respect to the unknown parameter is discussed to know the dependency of the temperature with the parameter. This paper signifies the effect of a prior model on the execution of the inverse algorithm at different noise levels. The unknown heat flux is estimated for the surrogated temperature and the estimates are reported as mean, Maximum a Posteriori (MAP) and standard deviation. The effect of a-priori information on the estimated parameter is also addressed. The standard deviation in the estimation process is referred to as the uncertainty associated with the estimated parameters.

MEG source localization under multiple constraints: An extended Bayesian framework

BackgroundFor years there has been a tremendous gap in our understanding of the mental health effects of deployment and the efforts by military forces at trying to minimize or mitigate these. Many military forces have recently systematized the mental support that is provided to support operational deployments. However, the rationale for doing so and the consequential allocation of resources are felt to vary considerably across North Atlantic Treaty Organisation (NATO) International Security Assistance (ISAF) partners. This review aims to compare the organization and practice of mental support by five partnering countries in the recent deployment in Afghanistan in order to identify and compare the key methods and structures for delivering mental health support, describe bottlenecks and illustrate new developments.MethodInformation was collected through document analysis and semi-structured interviews with key military mental healthcare stakeholders. The review resulted from close collaboration between key military mental healthcare professionals within the Australian Defense Forces (ADF), Canadian Armed Forces (CAF), United Kingdom Armed Forces (UK), Netherlands Armed Forces (NLD), and the United States Army (US). Key stakeholders were interviewed about the mental health support provided during a serviceperson's military career. The main items discussed were training, prevention, early identification, intervention, and aftercare in the field of mental health.ResultsAll forces reported that much attention was paid to mental health during the individual's military career, including deployment. In doing so there was much overlap between the rationale and applied methods. The main method of providing support was through training and education. The educative focus was to strengthen the mental resilience of individual soldiers while providing a range of mental healthcare services. All forces had abandoned standard psychological debriefing after critical incidents. Instead, by default, mental healthcare professionals acted to support the leader and peer led “after action” reviews. All countries provided professional mental support close to the front line, aimed at early detection and early return to normal activities within the unit. All countries deployed a mental health support team that consisted of a range of mental health staff including psychiatrists, psychologists, social workers, mental health nurses, and chaplains. There was no overall consensus in the allocation of mental health disciplines in theatre. All countries (except the US) provided troops with a third location decompression (TLD) stop after deployment, which aimed to recognize what the deployed units had been through and to prepare them for transition home. The US conducted in-garrison ‘decompression’, or ‘reintegration training’ in the US, with a similiar focus to TLD. All had a reasonably comparable infrastructure in the field of mental healthcare. Shared bottlenecks across countries included perceived stigma and barriers to care around mental health problems as well as the need for improving the awareness and recognition of mental health problems among service members. among service personnel as well as the need for improving the awareness and recognition of symptoms as being indicative of a mental health problems among servicemembers.ConclusionThis analysis demonstrated that in all five partners state-of-the-art preventative mental healthcare was included in the last deployment in Afghanistan, including a positive approach towards strengthening the mental resilience, a focus on self-regulatory skills and self-empowerment, and several initiatives that were well-integrated in a military context. These initiatives were partly/completely implemented by the military/colleagues/supervisors and applicable during several phases of the deployment cycle. Important new developments in operational mental health support are recognition of the role of social leadership and enhancement of operational peer support. This requires awareness of mental problems that will contribute to reduction of the barriers to care in case of problems. Finally, comparing mental health support services across countries can contribute to optimal preparation for the challenges of military deployment.

In this paper we present a probabilistic formalization of the instance-based learning approach. In our Bayesian framework, moving from the construction of an explicit hypothesis to a data-driven instancebased learning approach, is equivalent to averaging over all the (possibly infinitely many) individual models. The general Bayesian instance-based learning framework described in this paper can be applied with any set of assumptions defining a parametric model family, and to any discrete prediction task where the number of simultaneously predicted attributes is small, which includes for example all classification tasks prevalent in the machine learning literature. To illustrate the use of the suggested general framework in practice, we show how the approach can be implemented in the special case with the strong independence assumptions underlying the so called Naive Bayes classifier. The resulting Bayesian instance-based classifier is validated empirically with public domain data sets and the results are compared to the performance of the traditional Naive Bayes classifier. The results suggest that the Bayesian instancebased learning approach yields better results than the traditional Naive Bayes classifier, especially in cases where the amount of the training data is small.

Objective:Guided by Maslow’s Hierarchy of Needs, this study explored the impact of home-based social support services on the well-being of older adults in Turkey.Methods:A qualitative descriptive design was employed with 63 participants, including 25 older adults, 23 caregivers/family members, and 15 professionals from four major cities. Data were collected through semi-structured interviews and analyzed using MAXQDA 2024.Results:Home-based services were found to enhance both physical and psychological well-being. Nonetheless, chronic health conditions limited daily activities and social engagement. Older adults who maintained regular interactions with family, friends, and professionals demonstrated greater emotional resilience and strengthened social relationships.Discussion:Strengthening collaboration between families and professionals in home-based care is essential to improve older adults’ well-being and promote active aging. The findings underscore the need for inclusive, needs-oriented approaches in gerontological social work and aging policies.

Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example.

This paper reports the structural health monitoring benchmark study results for the Canton Tower using Bayesian methods. In this study, output-only modal identification and finite element model updating are considered using a given set of structural acceleration measurements and the corresponding ambient conditions of 24 hours. In the first stage, the Bayesian spectral density approach is used for output-only modal identification with the acceleration time histories as the excitation to the tower is unknown. The modal parameters and the associated uncertainty can be estimated through Bayesian inference. Uncertainty quantification is important for determination of statistically significant change of the modal parameters and for weighting assignment in the subsequent stage of model updating. In the second stage, a Bayesian model updating approach is utilized to update the finite element model of the tower. The uncertain stiffness parameters can be obtained by minimizing an objective function that is a weighted sum of the square of the differences (residuals) between the identified modal parameters and the corresponding values of the model. The weightings distinguish the contribution of different residuals with different uncertain levels. They are obtained using the Bayesian spectral density approach in the first stage. Again, uncertainty of the stiffness parameters can be quantified with Bayesian inference. Finally, this Bayesian framework is applied to the 24-hour field measurements to investigate the variation of the modal and stiffness parameters under changing ambient conditions. Results show that the Bayesian framework successfully achieves the goal of the first task of this benchmark study.

Drawing upon findings from many disciplines including sociology, communication, family studies, human development, psychology and anthropology-this book provides the first composite study of the whole family and of the complex interplay between self and collectivity in family life. It departs sharply from the traditional two-person, cause-effect models used in conventional studies, and attempts to delineate a social psychology of the family. This book undertakes to define and understand the nature of families, to point out ways of discerning different family characters, and to comprehend the processes by which these characters are established and maintained; by so doing, it introduces a new dimension into the study of family behavior and provides a framework within which meaningful investigations and practical applications can be pursued. This long-awaited fourth edition continues the goal of preceding editions: to understand families in terms of the kinds of interaction through which family life is constructed. Contributors drawn from a wide variety of disciplines sociology; communication; family studies; human development; psychology; anthropology; and social work - provide a range of authoritative and up-to-date sources on the family and interpersonal relations, including newly emergent forms of family organization. In providing a new framework for fruitful investigation and practical application, this volume contains the best available interdisciplinary work on the social psychology of the family.

Logit, probit and complementary log-log models are the most widely used models when binary dependent variables are available. Conventionally, these models have been frequentists. This paper aims to demonstrate how such models can be implemented relatively quickly and easily from a Bayesian framework using Gibbs sampling Markov chain Monte Carlo simulation methods in WinBUGS. We focus on the modeling and prediction of Down syndrome (DS) and Mental retardation (MR) data from an observational study at Kuwait Medical Genetic Center over a 30-year time period between 1979 and 2009. Modeling algorithms were used in two distinct ways; firstly, using three different methods at the disease level, including logistic, probit and cloglog models, and, secondly, using bivariate logistic regression to study the association between the two diseases in question. The models are compared in terms of their predictive ability via R2, adjusted R2, root mean square error (RMSE) and Bayesian Deviance Information Criterion (DIC). In the univariate analysis, the logistic model performed best, with R2 (0.1145), adjusted R2 (0.114), RMSE (0.3074) and DIC (7435.98) for DS, and R2 (0.0626), adjusted R2 (0.0621), RMSE (0.4676) and DIC (23120) for MR. In the bivariate case, results revealed that 7 and 8 out of the 10 selected covariates were significantly associated with DS and MR respectively, whilst none were associated with the interaction between the two outcomes. Bayesian methods are more flexible in handling complex non-standard models as well as they allow model fit and complexity to be assessed straightforwardly for non-nested hierarchical models.

Monitoring external loads on underground structures is crucial for structural health assessment. Inverting earth pressures from observable structural responses, such as deformation data, holds promise. However, existing methods often rely on presumptions about pressure complexity, which can be infeasible for many poorly performing in‐service infrastructures. This paper proposes a trans‐dimensional Bayesian method that simultaneously infers both the complexity and magnitude of earth pressures by parameterizing a set of a priori unknown variables, where the exact number of parameters remains undetermined. A Bayesian framework is employed to represent the posterior distribution of these parameters, with a trans‐dimensional Markov chain specifically designed for statistical inference of the distributed pressures. Case studies demonstrate that the proposed method outperforms traditional methods, which are limited by rigid presumptions. Furthermore, it is shown that the inferred pressures can reproduce comprehensive structural responses, such as internal forces, providing new tools and insights for structural health monitoring of underground infrastructures.

This paper describes a new Bayesian modeling and analysis method for emission computed tomography based on a novel multiscale framework. The class of multiscale priors has the interesting feature that the non-informative member yields the traditional maximum likelihood solution; other choices are made to reflect prior belief as to the smoothness of the unknown intensity. Remarkably, this Bayesian multiscale framework admits a novel maximum a posteriori (MAP) reconstruction procedure using an expectation-maximization (EM) algorithm, in which the EM update equations have simple, closed-form expressions. The potential of this new framework is assessed using the Zubal brain phantom and simulated SPECT studies.

We introduce a new approach for image filtering in a Bayesian framework, in this case the probability density function (pdf) of the likelihood function is approximated using the concept of non-parametric or kernel estimation. The method is also based on generalized Gaussian Markov random fields (GGMRF), a class of Markov random fields which are used as prior information into the Bayesian rule, which principal objective is to eliminate those effects caused by the excessive smoothness on the reconstruction process of images which are rich in contours or edges. Accordingly to the hypothesis made for the present work, it is assumed a limited knowledge of the noise pdf, so the idea is to use a non-parametric estimator to estimate such a pdf and then apply the entropy to construct the cost function for the likelihood term. The previous idea leads to the construction of Maximum a posteriori (MAP) robust estimators, since the real systems are always exposed to continuous perturbations of unknown nature. Some promising results of three new MAP entropy estimators (MAPEE) for image filtering are presented, together with some partial concluding remarks.