The positive role of authentic leadership in organizations negatively affected by cognitive diversity.

Workplace diversity has recently gained increasing significance and urgency in business organizations. This promotion may stem from information processing, and specifically from information elaboration. Information elaboration leverages diverse task-related information and skills possessed by members, fostering the exchange of diverse perspectives, elaborate discussions, and achieving high team performance. In this context, cognitive diversity, encompassing members' knowledge, skills, and perspectives, may have a positive impact. However, some previous studies suggest that cognitive diversity can lead to affective conflict and impede information processing. In organizations with highly homogeneous social and cultural backgrounds, cognitive diversity may not be effectively utilized in the information elaboration process, potentially yielding negative effects. Authentic leadership is recognized as a significant contributor to facilitating team processes including information processing, with various studies demonstrating its effectiveness. This study hypothesized that cognitive diversity negatively affects the information elaboration process, while authentic leadership has a positive effect. To test these hypotheses, we employed multilevel structural equation modeling analysis based on data collected from 375 respondents in 90 teams across various industries in Japan. The results showed that cognitive diversity negatively affects information elaboration at the individual level. By contrast, authentic leadership positively affects information elaboration at both individual and team levels. These findings suggest that the effect of cognitive diversity on information processing in the workplace may not always be positive, particularly in a sociocultural context that values homogeneity, as observed in Japanese organizations. This study advances the literature on authentic leadership by validating its effect on information elaboration and provides practical implications for diversity management. Additionally, it underscores the effectiveness of authentic leadership in leveraging team members' cognitive diversity to facilitate information elaboration.

Contextual factors associated with diarrhea among under-five children in the Gambia: a multi-level analysis of population-based data.

BACKGROUND: Diarrhea poses a significant threat to the lives of children in The Gambia, accounting for approximately 9% of all deaths among children under the age of five. Addressing and reducing child mortality from diarrhea diseases is crucial for achieving the Sustainable Development Goal (SDG) 3, specifically target 3.2, which aims to eliminate preventable deaths in newborns and children under the age of five by 2030. Thus, this research aims to assess the prevalence and contextual factors associated with diarrhea among under-five children in The Gambia. METHODS: This research employed secondary data from the 2019/20 Gambia Demographic Health Survey (GDHS). The study initially involved 8,362 women aged between 15 and 49 years. Of these, 6,929 women with children under five were included in this analysis. Data were analyzed using STATA with cross-tabulation and model fitting. Multilevel logistic regression was applied to accommodate the hierarchical structure of the demographic health survey data. The model comparison parameters were BIC, AIC, deviance, and LLR. Variables with a p-value less than 0.05 were selected for multivariable analysis. The statistical significance of the factors was determined using an adjusted odds ratio with a 95% confidence interval (CI) and a p-value of less than 0.05. RESULTS: The prevalence of diarrhea in under-five children was 53.2% in males and 46.8% in females. In the final model, Kerewan (aOR = 0.58; 95% CI = 0.33-0.98) and Basse (aOR = 0.59; 95% CI = 0.35-0.98) have significantly lower odds of childhood diarrhea compared to Banjul, female children show slightly lower, yet significant, odds of diarrhea compared to males (aOR = 0.96; 95% CI = 0.86-0.98), deliveries at government health centers are associated with higher odds of childhood diarrhea compared to home births (aOR = 1.24; 95% CI = 1.01-1.52). Mothers with post-secondary education had significantly lower odds of having children with diarrhea than those without any education (aOR = 0.50; 95% CI = 0.26-0.99) after controlling for confounders. CONCLUSION: The study findings indicate that several factors significantly impact the risk of childhood diarrhea in The Gambia. These factors include region of residence, sex of the child, place of delivery, and education level of the mother. The study suggests that existing interventions aimed at improving child health outcomes in the country should take into consideration these influential factors. Addressing these modifiable factors can enhance the effectiveness of interventions and promote better health outcomes for children in Gambia.

Multilevel determinants of physical violence among ever-partnered women in South Africa.

BACKGROUND: Violence against women continues to be a challenge in many countries. Many women suffer physical violence at the hands of their intimate partners and sometimes this leads to their deaths. This study aimed to examine the multilevel determinants of physical violence among ever-partnered women in South Africa. METHODS: We used data from the 2016 South Africa Demographic and Health Survey. The study has a weighted sample size of 4169 ever-partnered women aged 18-49 years, based on the domestic violence module. We included univariate, bivariate and multilevel logistic regression analysis. We included a two-level model to measure the relationship between the selected background characteristics and physical violence. RESULTS: The prevalence of physical violence among ever-partnered women was 20.6%. The bivariate findings showed that educational status, employment status, witness to inter-parental violence, partner's drinking habits, household wealth, educational difference, and province were statistically associated with physical violence. The multilevel analysis showed some evidence of between-cluster variation in physical violence. We found that age, education, employment status, witness to inter-parental violence, partner's drinking habits, household wealth, education difference, place of residence, and province were key predictors of physical violence. The odds of physical violence were more than two-fold in the Eastern Cape and Mpumalanga compared to Gauteng. CONCLUSION: The study highlighted various key factors explaining physical violence. The findings suggest the need for targeted interventions aimed at specific communities of women, such as those from the Eastern Cape and Mpumalanga, as well as interventions that will empower women and address gender inequalities.

Financial impacts of a housing order on commercial free range egg layers in response to highly pathogenic avian influenza.

Recent annual outbreaks of Highly Pathogenic Avian Influenza (HPAI) have led to mandatory housing orders on commercial free-range flocks. Indefinite periods of housing, after poultry have had access to range, could have production and financial consequences for free range egg producers. The impact of these housing orders on the performance of commercial flocks is seldom explored at a business level, predominantly due to the paucity of commercially sensitive data. The aim of this paper is to assess the financial and production impacts of a housing order on commercial free-range egg layers. We use a unique data set showing week by week performance of layers gathered from 9 UK based farms over the period 2020-2022. These data cover an average of 100,000 laying hens and include two imposed housing orders, in 2020/2021 and in 2021/22. We applied a random intercept linear regression to assess impacts on physical outputs and inputs, bird mortality and the impacts on revenue, feed costs and margin over feed cost. Feed use and feed costs per bird increased during the housing order which is a consequence of increased control over diet intake in housed compared to ranged birds. An increase in revenue was also found, ostensibly due to a higher proportion of large eggs produced, leading to a higher margin over feed cost. Overall, these large commercial poultry sheds were able to mitigate some of the potential adverse economic effects of housing orders. Potential negative impacts may occur dependant on the duration of the housing order and those farms with less control over their input costs.

Multi-Level Switching of Spin-Torque Ferromagnetic Resonance in 2D Magnetite.

2D magnetic materials hold substantial promise in information storage and neuromorphic device applications. However, achieving a 2D material with high Curie temperature (TC), environmental stability, and multi-level magnetic states remains a challenge. This is particularly relevant for spintronic devices, which require multi-level resistance states to enhance memory density and fulfil low power consumption and multi-functionality. Here, the synthesis of 2D non-layered triangular and hexagonal magnetite (Fe3O4) nanosheets are proposed with high TC and environmental stability, and demonstrate that the ultrathin triangular nanosheets show broad antiphase boundaries (bAPBs) and sharp antiphase boundaries (sAPBs), which induce multiple spin precession modes and multi-level resistance. Conversely, the hexagonal nanosheets display slip bands with sAPBs associated with pinning effects, resulting in magnetic-field-driven spin texture reversal reminiscent of "0" and "1" switching signals. In support of the micromagnetic simulation, direct explanation is offer to the variation in multi-level resistance under a microwave field, which is ascribed to the multi-spin texture magnetization structure and the randomly distributed APBs within the material. These novel 2D magnetite nanosheets with unique spin textures and spin dynamics provide an exciting platform for constructing real multi-level storage devices catering to emerging information storage and neuromorphic computing requirements.

Determinants of nutritional status of children aged 6-59 months in the case of Itang special woreda, Gambella, Ethiopia.

Nutritional status is one of the most important causes of improper physical and mental development in children. The study attempts to assess the factors affecting the severity status of children aged 6-59 months' malnutrition based on the weight-for-age anthropometric index (z-score) and examine between-kebeles-level differences in determinants of the nutritional status of children. A community-based, cross-sectional study design was conducted from October 12 to November 12, 2022. A sample of 397 children aged 6-59 months primary data by applying multi-stage clustered sampling technique was used by considering their heterogeneity. The data were entered by SPSS and analyzed by using R version 3.4.0 and STATA 14.2 statistical software package using a multilevel ordinal logistic regression model and inferences were conducted at a 5% significance level. The results show that birth interval ≥ 24 months (OR = 1.431253, 95% CI 1.221337 1.6763421, P-value = 0.008), economic status of households medium (OR = 16.21466, 95% CI 1.221403 1.423929, P-value = 0.000), economic status of households rich (OR = 223.2856, 95% CI 1.34295 2.582325, P-value = 0.000), employment status of the mother unemployed (OR = 0.2291348, 95% CI 0.0529511 0.9966281, P-value = 0.049), No toilet facility (bush field) (OR = 0.3163329, 95% CI 0.1825356 0.5481975, P-value = 0.000), number of household members (OR = 0.9100682, 95% CI 0.8313481 0.9967315, P-value = 0.042), breastfeeding < 12 months (OR = 0.53803, 95% CI 0.322315 0.898135, P-value = 0.018), educational level of father Primary (OR = 4.601687, 95% CI 1.758009 2.22053, P-value = 0.000), educational level of father Secondary above (OR = 99.65229, 95% CI 2.533502 4.788896, P-value = 0.000) and geographical area (kebeles) were found to be important factors that affect a child's nutritional status between 6 and 59 months. 15% of the overall variation is attributable to the Kebeles level, according to two-level multilevel ordinal logistic regressions with estimates of the variation attributable to the Kebeles level equal to 0.569 and an intraclass correlation coefficient of 0.15. Due to the nature of the response variable random intercept model with random coefficients fitted the data adequately in predicting the severity status of children aged 6-59 months' malnutrition for the multilevel ordinal logistic regression model analysis. So, the researcher recommended that implementing primary health care and nutrition programs that would fit each kebeles' features in Itang Special Woreda to safeguard children from nutritional deficiency.

Multilevel Intervention Stepped Wedge Designs (MLI-SWDs).

Multilevel interventions (MLIs) hold promise for reducing health inequities by intervening at multiple types of social determinants of health consistent with the socioecological model of health. In spite of their potential, methodological challenges related to study design compounded by a lack of tools for sample size calculation inhibit their development. We help address this gap by proposing the Multilevel Intervention Stepped Wedge Design (MLI-SWD), a hybrid experimental design which combines cluster-level (CL) randomization using a Stepped Wedge design (SWD) with independent individual-level (IL) randomization. The MLI-SWD is suitable for MLIs where the IL intervention has a low risk of interference between individuals in the same cluster, and it enables estimation of the component IL and CL treatment effects, their interaction, and the combined intervention effect. The MLI-SWD accommodates cross-sectional and cohort designs as well as both incomplete (clusters are not observed in every study period) and complete observation patterns. We adapt recent work using generalized estimating equations for SWD sample size calculation to the multilevel setting and provide an R package for power and sample size calculation. Furthermore, motivated by our experiences with the ongoing NC Works 4 Health study, we consider how to apply the MLI-SWD when individuals join clusters over the course of the study. This situation arises when unemployment MLIs include IL interventions that are delivered while the individual is unemployed. This extension requires carefully considering whether the study interventions will satisfy additional causal assumptions but could permit randomization in new settings.

Multi-level feature interaction image super-resolution network based on convolutional nonlinear spiking neural model.

Image super-resolution (ISR) is designed to recover lost detail information from low-resolution images, resulting in high-quality and high-definition high-resolution images. In the existing single ISR (SISR) methods based on convolutional neural networks (CNN), however, most of the models cannot effectively combine global and local information and are also easy to ignore the correlation between different hierarchical feature information. To address these problems, this study proposes a multi-level feature interactive image super-resolution network, which is constructed by the convolutional units inspired by nonlinear spiking mechanism in nonlinear spiking neural P systems, including shallow feature processing, deep feature extraction and fusion, and reconstruction modules. The different omni domain self-attention blocks are introduced to extract global information in the deep feature extraction and fusion stage and formed a feature enhancement module having a Transformer structure using a novel convolutional unit for extracting local information. Furthermore, to adaptively fuse features between different hierarchies, we design a multi-level feature fusion module, which not only can adaptively fuse features between different hierarchies, but also can better interact with contextual information. The proposed model is compared with 16 state-of-the-art or baseline models on five benchmark datasets. The experimental results show that the proposed model not only achieves good reconstruction performance, but also strikes a good balance between model parameters and performance.

Uneven primary healthcare supply of rural doctors and medical equipment in remote China: community impact and the moderating effect of policy intervention.

BACKGROUND: Unequal access to primary healthcare (PHC) has become a critical issue in global health inequalities, requiring governments to implement policies tailored to communities' needs and abilities. However, the place-based facility dimension of PHCs is oversimplified in current healthcare literature, and formulating the equity-oriented PHC spatial planning remains challenging without understanding the multiple impacts of community socio-spatial dynamics, particularly in remote areas. This study aims to push the boundary of PHC studies one step further by presenting a nuanced and dynamic understanding of the impact of community environments on the uneven primary healthcare supply. METHODS: Focusing on Shuicheng, a remote rural area in southwestern China, multiple data are included in this village-based study, i.e., the facility-level healthcare statistics data (2016-2019), the statistical yearbooks, WorldPop, and Chinese GDP's spatial distribution data. We evaluate villages' PHC service capacity using the number of doctors and essential equipment per capita, which are the major components of China's PHC delivery. The indicators describing community environments are selected based on extant literature and China's planning paradigms, including town- and village-level factors. Gini coefficients and local spatial autocorrelation analysis are used to present the divergences of PHC capacity, and multilevel regression model and (heterogeneous) difference in difference model are used to examine the driving role of community environments and the dynamics under the policy intervention. RESULTS: Despite the general improvement, PHC inequalities remain significant in remote rural areas. The village's location, aging, topography, ethnic autonomy, and economic conditions significantly influence village-level PHC capacity, while demographic characteristics and healthcare delivery at the town level are also important. Although it may improve the hardware setting in village clinics (coef. = 0.350), the recent equity-oriented policy attempts may accelerate the loss of rural doctors (coef. = - 0.517). Notably, the associations between PHC and community environments are affected inconsistently by this round of policy intervention. The town healthcare centers with higher inpatient service capacity (coef. = - 0.514) and more licensed doctors (coef. = - 0.587) and nurses (coef. = - 0.344) may indicate more detrimental policy effects that reduced the number of rural doctors, while the centers with more professional equipment (coef. = 0.504) and nurses (coef. = 0.184) are beneficial for the improvement of hardware setting in clinics. CONCLUSIONS: The findings suggest that the PHC inequalities are increasingly a result of joint social, economic, and institutional forces in recent years, underlining the increased complexity of the PHC resource allocation mechanism. Therefore, we claim the necessity to incorporate a broader understanding of community orientation in PHC delivery, particularly the interdisciplinary knowledge of the spatial lens of community, to support its sustainable development. Our findings also provide timely policy insights for ongoing primary healthcare reform in China.

Association between social capital and utilization of essential public health services among elderly migrants: a multilevel logistic study based on the 2017 China migrant dynamic survey (CMDS).

BACKGROUND: As the number of elderly migrants in China continues to grow, it is necessary to pay closer attention to their health and health services. Some studies have confirmed that social capital plays a significant role in the utilization of health services. Therefore, an in-depth exploration of the relationship between social capital and the utilization of essential public health services (EPHS) by elderly migrants will not only contribute to improving their overall health but also facilitate a more balanced development of public health service system in China. METHODS: Based on the cross-sectional data from the 2017 China Migrants Dynamic Survey (CMDS), this study examined the impact of social capital on the utilization of EPHS among elderly migrants. We evaluated social capital at two distinct levels: the individual and the community, and considered two dimensions of social capital: structural social capital (SSC) and cognitive social capital (CSC). The study aimed to delve into the impact of these forms of social capital on the utilization of EPHS among elderly migrants, and whether the migration range moderates this impact by multilevel logistic regression analysis. RESULTS: A total of 5,728 migrant elderly individuals were selected. The health records establishment rate and health education acceptance rate were approximately 33.0% and 58.6%, respectively. Social capital influenceed the utilization of EPHS among elderly migrants. Specifically, individual-level SSC and CSC have impacts on both the establishment of health records (OR = 1.598, 95%CI 1.366-1.869; OR = 1.705, 95%CI 1.433-2.028) and the acceptance of health education (OR = 1.345, 95%CI 1.154-1.567; OR = 2.297, 95%CI 1.906-2.768) among elderly migrants, while community-level SSC only affected the acceptance of health education (OR = 3.838, 95%CI 1.328-11.097). There were significant differences in individual-level SSC, health records, and health education among different migration range subgroups among elderly migrants. Migration range moderated the effect of social capital on the utilization of EPHS, crossing provinces could weaken the relationship between SSC and health education. CONCLUSIONS: Social capital is associated with a higher utilization rate of EPHS among elderly migrants. It is necessary to encourage them to actively participate in social activities, strengthen public services and infrastructure construction in the area, and improve their sense of belonging and identity.

Anesthetic Management of a Patient With Trisomy 18 Undergoing a Multilevel Spinal Fusion.

Trisomy 18 is the second most common autosomal trisomy aside from trisomy 21. Anesthesiologists were unlikely to manage such patients in the past, specifically those surviving later into childhood due to the 90% mortality rate within the first year of life and the lack of procedural options that were available. However, a paucity of literature regarding the anesthetic management of such patients exists. Trisomy 18 patients present a unique anesthetic challenge, given the presence of associated dysmorphic facial features and the involvement of multiple organ systems, leading to difficult airway and hemodynamic disturbances. In this case report, we present the anesthetic management of a 9-year-old patient with trisomy 18 undergoing a multilevel spinal fusion. Despite significant intraoperative hemorrhage, the patient was able to tolerate the procedure without complications, likely owing to the meticulous preoperative preparation and the patient's survival later into childhood. This case contributes to a small subset of literature which suggests that patients with trisomy 18 who survive later into childhood have an improved ability to tolerate general anesthesia.

LFMNet: a lightweight model for identifying leaf diseases of maize with high similarity.

Maize leaf diseases significantly impact yield and quality. However, recognizing these diseases from images taken in natural environments is challenging due to complex backgrounds and high similarity of disease spots between classes.This study proposes a lightweight multi-level attention fusion network (LFMNet) which can identify maize leaf diseases with high similarity in natural environment. The main components of LFMNet are PMFFM and MAttion blocks, with three key improvements relative to existing essential blocks. First, it improves the adaptability to the change of maize leaf disease scale through the dense connection of partial convolution with different expansion rates and reduces the parameters at the same time. The second improvement is that it replaces a adaptable pooling kernel according to the size of the input feature map on the original PPA, and the convolution layer to reshape to enhance the feature extraction of maize leaves under complex background. The third improvement is that it replaces different pooling kernels to obtain features of different scales based on GMDC and generate feature weighting matrix to enhance important regional features. Experimental results show that the accuracy of the LFMNet model on the test dataset reaches 94.12%, which is better than the existing heavyweight networks, such as ResNet50 and Inception v3, and lightweight networks such as DenseNet 121,MobileNet(V3-large) and ShuffleNet V2. The number of parameters is only 0.88m, which is better than the current mainstream lightweight network. It is also effective to identify the disease types with similar disease spots in leaves.

Food Insecurity and Outpatient Care Utilization Among Indonesian Older Adults.

This study aims to examine the complex relationship between household food insecurity and outpatient care utilization among Indonesian older adults. Individual-level data from the 2020 National Socioeconomic Survey (SUSENAS; n = 29 950 older adults aged 60 and over in poor self-rated health who are unable to do daily activities) and environmental-level data (n = 84 096 villages in a data aggregation across 514 municipalities) from the 2021 Village Potential Data Census Collection Survey (PODES) are used in a two-level binary logistic regression model. Older adults who live in severely food insecure households (odds ratio [OR] 1.963; CI [1.658, 2.319]) and lack of health insurance (OR 1.654; CI [1.556, 1.747]) are more likely of not utilizing outpatient care. The interclass correlation coefficient value is 0.162, indicating that primary health care services in every village improve outpatient care utilization. Food insecurity is the major determinant of reduced likelihood of outpatient care utilization, which can be explained by household resource constraints.

Multilevel Monte Carlo Methods for Stochastic Convection-Diffusion Eigenvalue Problems.

We develop new multilevel Monte Carlo (MLMC) methods to estimate the expectation of the smallest eigenvalue of a stochastic convection-diffusion operator with random coefficients. The MLMC method is based on a sequence of finite element (FE) discretizations of the eigenvalue problem on a hierarchy of increasingly finer meshes. For the discretized, algebraic eigenproblems we use both the Rayleigh quotient (RQ) iteration and implicitly restarted Arnoldi (IRA), providing an analysis of the cost in each case. By studying the variance on each level and adapting classical FE error bounds to the stochastic setting, we are able to bound the total error of our MLMC estimator and provide a complexity analysis. As expected, the complexity bound for our MLMC estimator is superior to plain Monte Carlo. To improve the efficiency of the MLMC further, we exploit the hierarchy of meshes and use coarser approximations as starting values for the eigensolvers on finer ones. To improve the stability of the MLMC method for convection-dominated problems, we employ two additional strategies. First, we consider the streamline upwind Petrov-Galerkin formulation of the discrete eigenvalue problem, which allows us to start the MLMC method on coarser meshes than is possible with standard FEs. Second, we apply a homotopy method to add stability to the eigensolver for each sample. Finally, we present a multilevel quasi-Monte Carlo method that replaces Monte Carlo with a quasi-Monte Carlo (QMC) rule on each level. Due to the faster convergence of QMC, this improves the overall complexity. We provide detailed numerical results comparing our different strategies to demonstrate the practical feasibility of the MLMC method in different use cases. The results support our complexity analysis and further demonstrate the superiority over plain Monte Carlo in all cases.

Us, them and we: How national and human identifications influence adolescents' ethnic prejudice.

Although there have been numerous studies on the relations between group identification and ethnic prejudice, it is less clear whether their associations reflect stable individual tendencies or rather situational or temporal fluctuations. This longitudinal multilevel study aimed to fill this gap by examining the between- and within-person associations of identification with the national and superordinate human groups and levels of prejudice against multiple ethnic minorities. A total of 883 Italian majority adolescents (Mage = 15.66, SD = 1.15 at T1, 49.7% females) completed questionnaires at four time points over the course of 1 year. Results showed that national identification was related to more prejudice at the between-person level but to decreases in prejudice at the within-person level. Additionally, human identification contributed to lower levels of and steeper decreases in prejudice at both the between- and within-person levels. Common and unique associations also emerged across different ethnic minority targets, but only for between-person effects. Overall, this study highlights the importance of distinguishing stable individual levels and momentary fluctuations of both ingroup identifications and ethnic prejudice in order to orient future interventions aimed at improving the quality of intergroup relationships.

Predictors of Complicated Grief During the COVID-19 Pandemic: A Cross-Classified Analysis.

The COVID-19 pandemic left many people grieving multiple deaths and at risk for developing symptoms of complicated grief (CG). The present study is a prospective examination of the role of neuroticism and social support in the development of CG symptoms. Findings from cross-classified multilevel models pointed to neuroticism as a risk factor for subsequent CG symptoms. Social support had a stress-buffering effect, emerging as a protective factor following the loss of a first degree relative. More recent loss and younger age of the deceased were both independently associated with heightened CG symptoms. Results from the present study provide insight into heterogeneity in CG symptom development at the between-person level, and variability in CG symptoms within individuals in response to different deaths. Findings could therefore aid in the identification of those at risk for the development of CG symptoms.

Understanding the Consequences of Collinearity for Multilevel Models: The Importance of Disaggregation Across Levels.

In multilevel models, disaggregating predictors into level-specific parts (typically accomplished via centering) benefits parameter estimates and their interpretations. However, the importance of level-specificity has been sparsely addressed in multilevel literature concerning collinearity. In this study, we develop novel insights into the interactivity of centering and collinearity in multilevel models. After integrating the broad literatures on centering and collinearity, we review level-specific and conflated correlations in multilevel data. Next, by deriving formal relationships between predictor collinearity and multilevel model estimates, we demonstrate how the consequences of collinearity change across different centering specifications and identify data characteristics that may exacerbate or mitigate those consequences. We show that when all or some level-1 predictors are uncentered, slope estimates can be greatly biased by collinearity. Disaggregation of all predictors eliminates the possibility that fixed effect estimates will be biased due to collinearity alone; however, under some data conditions, collinearity is associated with biased standard errors and random effect (co)variance estimates. Finally, we illustrate the importance of disaggregation for diagnosing collinearity in multilevel data and provide recommendations for the use of level-specific collinearity diagnostics. Overall, the necessity of disaggregation for identifying and managing collinearity's consequences in multilevel models is clarified in novel ways.

A system of predictive scores to evaluate the risk of multilevel noncontiguous spinal fractures in patients with traumatic cervical spine injury.

PURPOSE: To develop a predictive scoring system to identify traumatic cervical spine injury patients at a high risk of having multilevel noncontiguous spinal fractures. METHODS: This 12-year retrospective observational cohort study included 588 traumatic cervical spine-injured patients. Patients were categorized into two groups: patients with multilevel noncontiguous spinal fractures and patients without this remote injury. Potential risk factors were examined using multivariable analysis to derive a predictive risk score from independent predictors. Results are presented as odds ratio with a 95% confidence interval (95% CI). The accuracy of the calculated predicted score was demonstrated by the area under the receiver operating characteristic curve (AuROC). RESULTS: The incidence of noncontiguous fracture among the patients was 17% (100 of 588). The independent risk factors associated with multilevel noncontiguous spinal fractures were motor weakness, intracranial injury, intrathoracic injury, and intraabdominal injury. The AuROC of the prediction score was 0.74 (95% CI 0.69, 0.80). The patients were classified into three groups, low-risk group (score< 1), moderate-risk group (score 1-2.5), and high-risk group (score≥ 3), based on the predicted risk of multilevel noncontiguous spinal fractures. CONCLUSIONS: This tool can potentially help preventing the missed diagnosis of cervical spine injuries with multilevel noncontiguous spinal fractures. CT scans or MRI of the entire spine to investigate remote multilevel noncontiguous spinal fractures may have a role in cervical spine-injured patients who have at least one of the independent risk factors and are strongly suggested for patients with scores in the high-risk group.

A Multilevel Person-Centered Perspective on the Role of Job Demands and Resources for Employees' Job Engagement and Burnout Profiles.

The present study examined the configurations, or profiles, taken by distinct global and specific facets of job engagement and burnout (by relying on a bifactor operationalization of these constructs) among a nationally representative sample of Canadian Defence employees (n = 13,088; nested within 65 work units). The present study also adopted a multilevel perspective to investigate the role of job demands (work overload and role ambiguity), as well as individual (psychological empowerment), workgroup (interpersonal justice), supervisor (transformational leadership), and organizational (organizational support) resources in the prediction of profile membership. Latent profile analyses revealed five profiles of employees: Burned-Out/Disengaged (7.13%), Burned-Out/Involved (12.13%), Engaged (18.14%), Engaged/Exhausted (15.50%), and Normative (47.10%). The highest turnover intentions were observed in the Burned-Out/Disengaged profile, and the lowest in the Engaged profile. Employees' perceptions of job demands and resources were also associated with profile membership across both levels, although the effects of psychological empowerment were more pronounced than the effects of job demands and resources related to the workgroup, supervisor, and organization. Individual-level effects were also more pronounced than effects occurring at the work unit level, where shared perceptions of work overload and organizational support proved to be the key shared drivers of profile membership.

Characterizing Stress Processes by Linking Big Five Personality States, Traits, and Day-to-Day Stressors.

The accumulation of day-to-day stressors can impact mental and physical health. How people respond to stressful events is a key mechanism responsible for the effects of stress, and individual differences in stress responses can either perpetuate or prevent negative consequences. Most research on daily stress processes has focused on affective responses to stressors, but stress responses can involve more than just affect (e.g., behavior, cognitions). Additionally, most research has studied the role of neuroticism in shaping those responses, but many other individual differences are associated with stress. In this study, we more broadly characterized daily stress processes by expanding the nomological networks of stress responses to include Big Five personality states. We also linked those stress responses to all Big Five traits, as well as individual differences in stress variety, severity, and controllability. We studied a sample of participants (N = 1,090) who reported on stressful events, their appraisal of events in terms of severity and controllability, and their Big Five personality states daily for 8-10 days (N = 8,870 observations). Multi-level structural equation models were used to separate how characteristics of the perceived stressful situation and characteristics of the person play into daily stress processes. Results showed that (1) all Big Five personality states shift in response to perceived stress, (2) all Big Five personality traits relate to average levels of perceived stress variety, severity, and controllability, (3) individual differences in personality and average perceived stress variety and perceived severity relate to the strength of personality state responses to daily stress, albeit in a more limited fashion. Our results point to new pathways by which stressors affect people in everyday life and begin to clarify processes that may explain individual differences in risk or resilience to the harmful effects of stress.