Causal Model Research Articles

The impact of the nurse practice environment, workload, and professional support on job outcomes and standards of care at primary health care clinics in South Africa: A structural equation model approach

BackgroundThere is substantial evidence on the associations between a positive nurse practice environment and improved nurse and patient outcomes, as well as the factors that mediate these associations, in high-income countries and in hospital settings. The knowledge gaps in African and primary health care settings motivated this empirical study. ObjectiveThe objective of this study was to examine the impact of the dimensions of the nurse practice environment, specifically human resource management, foundations for nursing care, and participation in clinic affairs, on job outcomes and standards of care. DesignA cross-sectional study was conducted between November 2021 and June 2022. Setting180 primary health care clinics in two South African provinces of Gauteng and North West. Participants665 nurses of all categories. MethodsA causal model was developed with pathways between the nurse practice environment dimensions and the outcomes of job satisfaction, intention to leave, and standards of care. A set of standardised instruments was used to measure the study variables. Using structural equation modelling, workload and professional support were tested as potential mediators between the nurse practice environment and the outcome variables. ResultsThe nurses scored the domain of foundations for nursing care 71.2 out of 100 on average, indicating high agreement, while the mean scores for nurses’ participation in clinic affairs and human resources management were lower at 68.0 and 61.7 respectively. Although nurses expressed moderate satisfaction with professional support (67.7), they were less satisfied with their workload (52.2). The mean score of overall job satisfaction was moderate (58.9), with 53.8 % of the nurses reporting that they intended leaving the clinic where they were working. Thirty-six percent intended leaving the nursing profession, indicating low intention to stay. The final mediation model was judged to fit the data adequately based on goodness-of-fit indices, confirming that workload and professional support had a mediating role between the nurse practice environment dimensions of interest and both nurses’ job outcomes and standards of care. ConclusionsWe have highlighted the value of supportive practice environments, effective workload management, and enhanced professional support in improving nurses’ job outcomes and satisfaction with standards of care. Improving nurses’ practice environments at primary health care level may have a wide-ranging impact on the performance of the health system. Therefore, primary health care facility managers should ensure that workload is distributed equitably, professional support for nurses is enhanced, and the overall work environment is improved.

A follow up report validating long term predictions of the COVID-19 epidemic in the UK using a dynamic causal model.

This paper asks whether Dynamic Causal modelling (DCM) can predict the long-term clinical impact of the COVID-19 epidemic. DCMs are designed to continually assimilate data and modify model parameters, such as transmissibility of the virus, changes in social distancing and vaccine coverage-to accommodate changes in population dynamics and virus behavior. But as a novel way to model epidemics do they produce valid predictions? We presented DCM predictions 12 months ago, which suggested an increase in viral transmission was accompanied by a reduction in pathogenicity. These changes provided plausible reasons why the model underestimated deaths, hospital admissions and acute-post COVID-19 syndrome by 20%. A further 12-month validation exercise could help to assess how useful such predictions are. we compared DCM predictions-made in October 2022-with actual outcomes over the 12-months to October 2023. The model was then used to identify changes in COVID-19 transmissibility and the sociobehavioral responses that may explain discrepancies between predictions and outcomes over this period. The model was then used to predict future trends in infections, long-COVID, hospital admissions and deaths over 12-months to October 2024, as a prelude to future tests of predictive validity. Unlike the previous predictions-which were an underestimate-the predictions made in October 2022 overestimated incidence, death and admission rates. This overestimation appears to have been caused by reduced infectivity of new variants, less movement of people and a higher persistence of immunity following natural infection and vaccination. despite an expressive (generative) model, with time-dependent epidemiological and sociobehavioral parameters, the model overestimated morbidity and mortality. Effectively, the model failed to accommodate the "law of declining virulence" over a timescale of years. This speaks to a fundamental issue in long-term forecasting: how to model decreases in virulence over a timescale of years? A potential answer may be available in a year when the predictions for 2024-under a model with slowly accumulating T-cell like immunity-can be assessed against actual outcomes.

Social media and youth mental health: Simple narratives produce biased interpretations.

Many academics and pundits contend that social media use is the primary cause of an international youth mental health crisis. However, these claims often rely on correlational evidence, ignoring the confounding effects of developmental, environmental, social, and psychological factors that influence mental health. This oversimplifies the complex etiology of mental health problems. We call for a more nuanced understanding of the role of social media in youth mental health that avoids oversimplification. Additionally, we urge researchers to move beyond vague, narrative-driven verbal theories and encode them into precise, testable causal models. Using simulation techniques and specification curve analyses, we show how misspecified models that ignore these confounding factors can lead to biased conclusions about social media's adverse effects. This simplistic focus on social media use diverts attention from the broader factors contributing to youth mental health problems, hindering the development of effective interventions and support. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Dynamic BIM Adoption Impact on Contract Cost Variance Factors Using PLS-SEM Techniques

This paper investigates the Building Information Modeling (BIM) adoption impact on the factors of Contract Cost Variance (CCV) over time. The study considers qualitative and quantitative data to identify the most common causes of CCV through pre-tendering. A partial least square-structure model (PLS-SEM) procedure was used to develop a causal model and rank CCV factors based on their effect, partially based on prior survey raw data conducted in 2022 and the data from 94 projects. Construction industry experts assessed the prior five-year rate of BIM adoption on construction projects to infer the expected trend in BIM adoption in the future (until 2037). Based on the causal model of CCV factors and the future rates of BIM adoption, the dynamic impact of BIM on CCV factors over time was modeled and analyzed. The analysis shows that BIM reduces CCV over time by improving Estimator Performance (EP), Information Quality (IQ), and contractual procedure (CP). The results showed that the CP, EP, and EF have directly impacted CCV, and the PC and IQ indirectly affect the CCV. This paper considers the temporal aspect, examining how the impact of BIM on CCV factors evolves. This dynamic analysis is crucial for long-term strategic planning in construction management.

Exploring the impact of internet usage on individuals’ social status discordance: evidence from China

This study examines the impact of internet usage frequency and behavior on social status discordance using CSS2021 data, and adopts a causal random forest model to test the heterogeneity of the impact of internet usage on social status discordance. The results reveal a clear inconsistency between objective and subjective social status among Chinese residents. In addition, increasing internet usage frequency can make actors more inclined to underestimate their social status. Moreover, capital enhancement through the internet significantly reduces the likelihood of status inflation. Finally, individuals with higher social status are less likely to have an upward bias in status cognition than those with lower social status when they use the internet frequently and use it for capital enhancement. These findings advance our understanding of social status identification among Chinese residents and provide different perspectives for studying social stratification in the digital age.

Survival after surgery beyond total mesorectal excision for primary locally advanced rectal cancer, a population-based study

BackgroundThe aim of this study was to compare relative survival in non-metastatic rectal cancer clinically staged as T3-T4 requiring beyond total mesorectal excision (TME) to that after standard TME. MethodsThis population-based study included all patients operated with anterior resection, abdominoperineal excision or Hartmann's procedure for non-metastatic rectal cancer clinically staged as T3-T4 in Sweden between 2009 and 2018. Relative survival was analysed in relation to surgery beyond TME (bTME), which was subcategorized as bTME- and bTME + to account for extent of resection. In all survival analyses, follow-up started at 90 days after surgery. Based on a causal model defined a priori excess mortality rate ratios (EMRR) were estimated using Poisson regression. ResultsOf 8272 included patients 1220 (14.7 %) were operated bTME. In a model adjusted for age and sex bTME was associated with higher excess mortality compared to standard TME (EMRR: 1.76, 95%CI:1.52–2.04). This association persisted after additional adjustment for tumour characteristics, neoadjuvant therapy and hospital volume (EMRR: 1.32, 95%CI:1.11–1.56) and was mainly attributable to restricted relative survival after bTME- (EMRR: 1.42, 95%CI:1.18–1.72) as EMRR after bTME+ was 1.07 (95%CI:0.80–1.44). ConclusionThis national population-based study showed inferior relative survival after bTME compared to standard TME in non-metastasized rectal cancer cT3-cT4. Unexpectedly this difference was mainly seen after bTME of limited extent.

Simulation model to analyze the impact of work on absenteeism

AbstractShoe manufacturing companies often use overtime work but neglect the impacts and importance of physical recovery time. Ergonomic methods aim to analyze this, but they focus on deterministic aspects, which limits their ability to evaluate working conditions amid variations over time. This research explores how a simulation model can mitigate these limitations and enhance analysis of overtime and physical recovery on worker absenteeism. The objective was developed a simulation model using System Dynamics (SD) to represent working conditions and assess the influence of overtime and recovery time in Brazil's footwear industry. An Ergonomic Analysis of Work was conducted in a large company's production cell. Using SD, were constructed a causal and simulation model to analyze three scenarios. An additional hour of work increased physical overload by 44%, leading to 5, 4 leave requests, and 48 days of absenteeism per year. Increasing recovery time by 15 min reduced overload to 38,96%, resulting in 4, 9 leave requests and 13,68 days of absenteeism. The SD simulation model mitigated the limitations of ergonomic methods in understanding the dynamic relationships over time, emphasizing the importance of actively managing overtime and physical recovery time.

Proportionality, Determinate Intervention Effects, and High-Level Causation

AbstractStephen Yablo’s notion of proportionality, despite controversies surrounding it, has played a significant role in philosophical discussions of mental causation and of high-level causation more generally. In particular, it is invoked in James Woodward’s interventionist account of high-level causation and explanation, and is implicit in a novel approach to constructing variables for causal modeling in the machine learning literature, known as causal feature learning (CFL). In this article, we articulate an account of proportionality inspired by both Yablo’s account of proportionality and the CFL account of variable construction. The resulting account has at least three merits. First, it illuminates an important feature of the notion of proportionality, when it is adapted to a probabilistic and interventionist framework. The feature is that at the center of the notion of proportionality lies the concept of “determinate intervention effects.” Second, it makes manifest a virtue of (common types of) high-level causal/explanatory statements over low-level ones, when relevant intervention effects are determinate. Third, it overcomes a limitation of the CFL framework and thereby also addresses a challenge to interventionist accounts of high-level causation.

Causal technological model for predicting void fraction and energy consumption in material extrusion process of polylactic acid

The rapid expansion of Additive Manufacturing (AM) technologies within the framework of Industry 4.0 raises important questions about their sustainability. Specifically, the widespread use of Material Extrusion (MEx) processes for polymeric materials necessitates a thorough evaluation of the possible balance between performance efficiency and sustainability, highlighting the need for further research into optimizing process parameters to ensure high product quality and reduced energy consumption. The present work focuses on Fused Filament Fabrication (FFF) technology, investigating the relationship between sustainability and print quality. A causal technology model is proposed to elucidate this relationship, underlining the impact of process parameters on defect generation and energy use. A real-time monitoring system for energy consumption measurements was employed to create an energy model that accounts for each component of the CreatBot F430 printer. An image analysis procedure was performed on a special configuration of the HIROX RH-2000 digital microscope to determine the void fraction of each sample. An experimental campaign was conducted, producing single-layer samples in polylactic acid (PLA) following a full factorial plan with four process parameters. A void fraction of up to 18 % was observed, with energy consumption per sample ranging between 638 J and 8843 J. Statistical analysis was performed to assess the impact of each process parameter and to determine operational ranges through the Response Surfaces Method. These results showed good agreement with the predicted conditions, demonstrating the model's effectiveness in supporting decision-making processes in technology selection.

Organizational Performance Improvement Model Based on Talent Management Through Religiosity Organizational Citizenship Behavior

Purpose: This research aims to determine the influence of talent management on organizational performance; the influence of religiosity organizational citizenship behavior as a mediator on organizational performance; the influence of digital literacy as a mediator on organizational performance; the influence of talent management, digital literacy and religiosity organizational citizenship behavior simultaneously on employee performance and employee performance. organization, and designing a model for improving organizational performance through digital literacy and religiosity organizational citizenship behavior as mediators Theoretical Framework: Drawing from the practice-based view theory, this research focuses on several constructs and relationships between talent management, digital literacy and organizational citizenship behavior in ASNs in government agencies which have not yet been widely studied. This research is an effort to develop a model for improving organizational performance through talent management and ASN digital literacy and also combines aspects of organizational citizenship behavior which are based on religious values, which are still rarely used by previous researchers. The research method used is an explanatory survey approach to explain the relationship between variables through hypothesis testing in the field. Research that uses descriptive surveys and explanatory survey methods is carried out by collecting information from samples directly at the scene of the incident (empirical) through questionnaires with the aim of finding out the opinions of the samples regarding the research problem. And this research uses the Partial Least Squares Structural Equation Modeling analysis technique (PLS-SEM), to model many variables in research Results and Discussion: To examine the influence of mediator variables, Partial Least Squares Structural Equation Modeling analysis technique (PLS-SEM),to estimate the causal relationship between variables (causal model) that has been determined previously based on the theory. Answering research questions requires correct statistical data processing. The results of this research will be more powerful if adequate statistical data processing is used. Scientifically based research shows that findings can be evaluated and proven, and if other researchers used the same procedures, the results would almost certainly be the same. As a result, researchers chose statistical data processing techniques to use in these calculations. The result of research. Based on the results of research regarding "Model for Improving Organizational Performance Based on Talent Management Through Religious Organizational Citizen Behavior", the following results were obtained: Overview of talent management, religiosity organizational citizenship behavior, digital literacy, employee performance and organizational performance in Regency/City Government Agencies throughout West Java Province are in the high/good category, which means they are running in accordance with agency procedures. Talent management has a significant positive effect on organizational performance in Regency/City Government Agencies throughout West Java Province. This is based on the results of direct effect hypothesis testing, the t-statistics results obtained were 2.482 and the significance results (pvalue) obtained were 0.007 (0.007<0.05), so Ho was rejected and Ha was accepted. Religious organizational citizenship behavior is unable to mediate the influence of talent management on employee performance. This is based on the results of testing the indirect effect hypothesis, obtaining a significance result (pvalue) of 0.053 (0.053>0.05). Meanwhile, digital literacy is able to mediate the influence of Talent Management on Employee Performance. This is based on the results of testing the indirect effect hypothesis, obtained a significance result (pvalue) of 0.000 (0.000<0.05). Digital literacy, religiosity organizational citizenship behavior and employee performance are able to mediate the influence of talent management on organizational performance. This is based on the results of testing the indirect effect hypothesis, obtained a significance result (pvalue) <0.05. There is an increase in organizational performance through digital literacy and religiosity organizational citizenship behavior in talent management. Implications: The research results show that religiosity organizational citizenship behavior, digital literacy and employee performance are variables that can mediate the influence of talent management on organizational performance in Regency/City Government Agencies throughout West Java Province.

Investigating the shared genetic architecture between depression and subcortical volumes

Depression, a widespread and highly heritable mental health condition, profoundly affects millions of individuals worldwide. Neuroimaging studies have consistently revealed volumetric abnormalities in subcortical structures associated with depression. However, the genetic underpinnings shared between depression and subcortical volumes remain inadequately understood. Here, we investigate the extent of polygenic overlap using the bivariate causal mixture model (MiXeR), leveraging summary statistics from the largest genome-wide association studies for depression (N = 674,452) and 14 subcortical volumetric phenotypes (N = 33,224). Additionally, we identify shared genomic loci through conditional/conjunctional FDR analyses. MiXeR shows that subcortical volumetric traits share a substantial proportion of genetic variants with depression, with 44 distinct shared loci identified by subsequent conjunctional FDR analysis. These shared loci are predominantly located in intronic regions (58.7%) and non-coding RNA intronic regions (25.4%). The 269 protein-coding genes mapped by these shared loci exhibit specific developmental trajectories, with the expression level of 55 genes linked to both depression and subcortical volumes, and 30 genes linked to cognitive abilities and behavioral symptoms. These findings highlight a shared genetic architecture between depression and subcortical volumetric phenotypes, enriching our understanding of the neurobiological underpinnings of depression.

Causal Modeling and Thermodynamics: Towards a new convergence of the two fields

In 1824, Nicolas Léonard Sadi Carnot paid for the publication of his first book. Unfortunately it sparked little interest, and the young engineer never published another. In quick succession, Carnot served in the military, suffered from scarlet fever, mania, and cholera, and passed away in obscurity at age 36. Two centuries have elapsed since Carnot published his only book. Recognition came later. In particular, Carnot's reasoning inspired scientists to formulate the first and second laws of Thermodynamics. The new science that has emerged around these physical laws is nothing short of breathtaking. Yet, with success and growth, critical attention and skepticism have followed. In 1924, Louis de Broglie lauded the first law of Thermodynamics, while remaining more reserved to wards the second. The first law builds on a long history rooted in Causal Modeling, while the second does less so. Today, physicists such as Adrian Bejan continue praising Thermodynamics but contend that some formulations of the second law may have attracted broken science. The present article revisits this history in an attempt to cut through some of the fog. As an outcome of this re-evaluation, the article outlines a new convergence of Thermodynamics and Causal Modeling.

GU-Net: Causal relationship-based generative medical image segmentation model

Due to significant anatomical variations in medical images across different cases, medical image segmentation is a highly challenging task. Convolutional neural networks have shown faster and more accurate performance in medical image segmentation. However, existing networks for medical image segmentation mostly rely on independent training of the model using data samples and loss functions, lacking interactive training and feedback mechanisms. This leads to a relatively singular training approach for the models, and furthermore, some networks can only perform segmentation for specific diseases. In this paper, we propose a causal relationship-based generative medical image segmentation model named GU-Net. We integrate a counterfactual attention mechanism combined with CBAM into the decoder of U-Net as a generative network, and then combine it with a GAN network where the discriminator is used for backpropagation. This enables alternate optimization and training between the generative network and discriminator, enhancing the expressive and learning capabilities of the network model to output prediction segmentation results closer to the ground truth. Additionally, the interaction and transmission of information help the network model capture richer feature representations, extract more accurate features, reduce overfitting, and improve model stability and robustness through feedback mechanisms. Experimental results demonstrate that our proposed GU-Net network achieves better segmentation performance not only in cases with abundant data samples and relatively simple segmentation targets or high contrast between the target and background regions but also in scenarios with limited data samples and challenging segmentation tasks. Comparing with existing U-Net networks with attention mechanisms, GU-Net consistently improves Dice scores by 1.19%, 2.93%, 5.01%, and 5.50% on ISIC 2016, ISIC 2017, ISIC 2018, and Gland Segmentation datasets, respectively.

Development of optimised causal AI and its application in retail banking

Causal artificial intelligence (AI) has emerged as a promising approach in machine learning (ML), as it considers not only correlations but also cause-and-effect relationships in data, resulting in more human-like decision making. The pivotal stages within causal AI involve causal discovery and inferencing, each playing a crucial role in extracting meaningful insights from the data. In the realm of causal discovery, various algorithms have been developed to uncover the underlying cause-and-effect structures within data sets. A notable limitation, however, surfaces when attempting to apply these algorithms to data sets characterised by binary variables. This constraint prompts a crucial examination of the current methodologies and calls for innovative solutions that can seamlessly navigate the complexities of binary variable data sets. This paper proposes an optimised causal discovery algorithm that is integrated with the causal inference method based on the estimation of conditional average treatment effects (CATE) scores. The results present the potential of causal AI in terms of incremental impact on the predictive capability of AI/ML models. And the incremental impact is elucidated by comparing conventional propensity-based modelling and causal AI-based modelling by means of a use case in the field of retail banking.

Mapping the vicious cycles of community gold mining (CGM): a case study of the CGM sites at Sukabumi Regency, Indonesia

Abstract Community Gold Mining (CGM) in Indonesia faces significant challenges, with a specific concern being the use of mercury. Mercury is a highly toxic chemical commonly utilized in the Trommel Mercury (TM) gold extraction method, known locally as the Glundung method. Although the government has initiated programs to reduce mercury usage, such as encouraging researchers to develop non-mercury gold extraction methods, progress has been slow, and the impact has been limited. The growth of new CGM sites is outpacing these efforts, leading to an increased use of mercury and unmanageable chemical risks. Previous research has identified a vicious cycle within the CGM sector. However, no existing model illustrates this cycle. This study seeks to map the scope of CGM at its essential stages and translate them into variables to create a causal and basic model. However, Sukabumi Regency in Indonesia hosts numerous CGM sites, and a case study was conducted in the Simpenan Sub-District between 2018 and 2020. A recent site visit in August 2023 revealed continued growth in CGM site numbers within the broader area. This growth corresponds to an increase in mercury released into the environment, which poses a growing threat to public health. The study employed ArcGIS and Powersim 10 System Dynamics Software, utilizing data collected through observations, investigative methods, and reference studies. The results include two significant contributions: first, a model of current CGM activities in the form of a Causal Loops Diagram (CLD) called “the Turtle Map CLD Model of the CGM”. Second, a model depicting “the vicious cycle of CGM” highlights problematic stages within CGM. Both models represent the current state of CGM in Indonesia, showcasing the existence of vicious cycles in ongoing CGM sites. These models can guide future efforts to identify progressive solutions, especially in support of programs aimed at reducing mercury usage.

Fruit resources shape sexual selection processes in a lek mating system.

The degree to which within-population variation in sexual trait expression relates to resource heterogeneity remains poorly explored. This is particularly true in lek-mating species, where genetic explanations for male phenotypic variance and mating success are dominant. Here, we demonstrate a link between fine-scale fruit resource availability and indices of male mating success in the white-bearded manakin (Manacus manacus), a lek-mating frugivorous bird that produces energetically costly courtship displays. We used motion-activated camera traps to monitor male display behaviour and female visitation at male courts while concurrently conducting twice-monthly fruit surveys around courts. We observed significant variability in ripe fruit biomass among display courts and leks, and mean fruit biomass at courts significantly predicted male display rates. In turn, male display rate was the strongest predictor of female visitation to courts. Causal modelling supported the hypothesis that hyper-local fruit availability indirectly affects female visitation via its direct effects on male display rate. The demonstration that resource availability at fine spatial scales predicts display rate in a lekking organism-for which resource-related variables are typically not considered to play important roles in shaping male reproductive variance-has implications for the expression, honesty and maintenance of sexually selected traits under fluctuating ecological conditions.

Simpson’s paradox beyond confounding

Simpson’s paradox (SP) is a statistical phenomenon where the association between two variables reverses, disappears, or emerges, after conditioning on a third variable. It has been proposed (by, e.g., Judea Pearl) that SP should be analyzed using the framework of graphical causal models (i.e., causal DAGs) in which SP is diagnosed as a symptom of confounding bias. This paper contends that this confounding-based analysis cannot fully capture SP: there are cases of SP that cannot be explained away in terms of confounding. Previous works have argued that some cases of SP do not require causal analysis at all. Despite being a logically valid counterexample, we argue that this type of cases poses only a limited challenge to Pearl’s analysis of SP. In our view, a more powerful challenge to Pearl comes from cases of SP that do require causal analysis but can arise without confounding. We demonstrate with examples that accidental associations due to genetic drift, the use of inappropriate aggregate variables as causes, and interactions between units (i.e., inter-unit causation) can all give rise to SP of this type. The discussion is also extended to the amalgamation paradox (of which SP is a special form) which can occur due to the use of non-collapsible association measures, in the absence of confounding.

Reimagining Reason’s Swiss Cheese Accident Causation Model

Reimagining Reason’s Swiss Cheese Accident Causation Model

Causal model of factors influencing the fundraising in Thai sports industry: Investment ecosystem evaluation for scale-up to innovation-driven enterprises

This pioneering research aimed to verify the causal model of factors influencing fundraising in Thai sports industry. Investment ecosystem and theory of planned behavior were employed to obtain understanding of investment situations in this industry. A mixed-method research approach was also utilized. In qualitative, seven experts were interviewed to drawing the structure of the causal model. In quantitative, the top 798 sport investors responded to modified rating-scale questionnaires for confirming indicators of underlying factors in the causal model. Analysis based on grounded theory revealed in the causal model that policy, market, culture, finance, supports, and human capital are antecedent factors that induce attitude, perceived value, and behavioral intention. Fundraising behavior was directly affected by behavioral intention. Confirmatory factor analysis supported that all indicators were appropriately loaded into 10 factors. Structural equation modelling of the causal model indicated that finance, human capital, and attitudes played a vital role in fundraising. The causal model showed that policymakers and entrepreneurs in Thailand will benefit from scaling the industry to innovation-driven enterprises.

The Impact of Sport Social Media Users’ Use Motivation on Leisure Flow, Acceptance Attitude, and Sports Values: Verification of Causal Model

The Impact of Sport Social Media Users’ Use Motivation on Leisure Flow, Acceptance Attitude, and Sports Values: Verification of Causal Model