Assessing the Impact of Different Depression Treatment Success Metrics on Organizational Performance.
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ABSTRACT:
SUBMITTER: Carlo AD
PROVIDER: S-EPMC8249313 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:When multiple institutions with strong sustainability initiatives use a new environmental impact assessment tool, there is an impulse to compare. The first seven institutions to calculate nitrogen footprints using the Nitrogen Footprint Tool have worked collaboratively to improve calculation methods, share resources, and suggest methods for reducing their footprints. This article compares those seven institutions' results to reveal the common and unique drivers of institution nitrogen footprints. The footprints were compared by scope and sector, and the results were normalized by multiple factors (e.g., population, amount of food served). The comparisons found many consistencies across the footprints, including the large contribution of food. The comparisons identified metrics that could be used to track progress, such as an overall indicator for the nitrogen sustainability of food purchases. The comparisons also pointed to differences in system bounds of the calculations, which are important to standardize when comparing across institutions. The footprints were influenced by factors both within and outside of the institutions' ability to control, such as size, location, population, and campus use. However, these comparisons also point to a pathway forward for standardizing nitrogen footprint tool calculations, identifying metrics that can be used to track progress, and determining a sustainable institution nitrogen footprint.
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Project description:BackgroundThe job performance of individual employees determines the overall performance of an organization, and organizational support is known as an important resource at the organizational level to enhance job performance. Although nursing scholars have confirmed the crucial role of organizational support in enhancing job performance, there are no studies on whether psychological capital and organizational identification mediate the association between organizational support and job performance. The purpose of this study is to investigate the influence of organizational support, psychological capital, and organizational identification on nurses' job performance.MethodsA cross-sectional survey was conducted among 455 nurses from 21 public hospitals in China. Instruments were perceived organizational support scale, task performance scale, contextual performance scale, Nurse Psychological Capital Questionnaire, and Organizational Identification Questionnaire. Survey data were analyzed using SPSS and AMOS, and hypotheses were tested using path model analysis.ResultsNurses' perceived organizational support, psychological capital, organizational identification, and task/contextual performance were positively correlated in every two variables. Psychological capital played an important mediating role in perceived organizational support and task/contextual performance, as well as organizational identification. The multi-mediating effect of psychological capital and organizational identification on the relationship between organizational support and task/contextual performance were 0.14 and 0.25, respectively.ConclusionsThere was a positive correlation between organizational support and job performance among nurses. Psychological support, organizational identification and contextual performance played a chain mediation role in the relationship between organizational support on task performance in nurses. Nursing managers should pay more attention to enhancing nurses' psychological capital and organizational identification through effective interventions to improve nurses' job performance.
| S-EPMC10375763 | biostudies-literature
Project description:BackgroundVarious metrics predicated on Patient Health Questionnaire-9 (PHQ-9) scores denote depression "response" or "remission" over time, but few have been empirically validated. We compare the associations of depression response and remission metrics with concomitant clinical improvement in patient-centered outcomes (PCOs).SubjectsSecondary analysis of PHQ-9 and PCO data from the treatment arm (n=906) of the Improving Mood-Promoting Access to Collaborative Treatment (IMPACT) trial.Research designWe conducted univariate correlations between 9 depression treatment metrics and 4 PCOs. For each PCO, we specified a multivariate linear fixed-effects regression model with penalized LASSO (least absolute shrinkage and selection operator) variable selection that included parameters for each incremental absolute PHQ-9 decrease between 0 and 16 points. Model predictive properties were assessed using a split sample analysis.ResultsThere was a notable variation in depression improvement rates across metrics. Each metric was significantly associated with PCOs in univariate analyses. In the multivariate models, the cumulative likelihood of PCO improvement was most improved by absolute PHQ-9 score decreases of 7-9 and 14-16 points. The multivariate models showed greater area under the curve (0.671-0.804) in out-of-sample predictions of PCO changes than the univariate models (0.529-0.649).ConclusionsChoice of depression response metric impacts observed response and remission rates, though PCOs tend to improve with depression improvement regardless of metric choice. Absolute incremental PHQ-9 score decreases are broadly associated with an increased likelihood of favorable PCO scores. Our findings support a novel PHQ-9 metric defined by an absolute score change of 8 points or greater.
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Project description:Previous work suggests that major depressive disorder (MDD) is associated with disturbances in global connectivity among brain regions, as well as local connectivity within regions. However, the relative importance of these global versus local changes for successful antidepressant treatment is unknown. We used multiscale entropy (MSE), a measure of brain signal variability, to examine how the propensity for local (fine scale MSE) versus global (coarse scale MSE) neural processing measured prior to antidepressant treatment is related to subsequent treatment response.We collected resting-state EEG activity during eyes-open and closed conditions from unmedicated individuals with MDD prior to antidepressant pharmacotherapy (N = 36) as well as from non-depressed controls (N = 36). Treatment response was assessed after 12 weeks of treatment using the Montgomery-Åsberg Depression Rating Scale (MADRS), at which time participants with MDD were characterized as either responders (? 50% MADRS decrease) or non-responders. MSE was calculated from baseline EEG, and compared between controls, future treatment responders and non-responders. Putative interactions with the well-documented age effect on signal variability (increased reliance on local neural communication with increasing age, indexed by greater finer-scale variability) were assessed.Only in responders, we found that reduced MSE at fine temporal scales (especially fronto-centrally) and increased MSE diffusely at coarser temporal scales was related to the magnitude of the antidepressant response. In controls and MDD non-responders, but not MDD responders, there was an increase in MSE with age at fine temporal scales and a decrease in MSE with age at coarse temporal scales.Our results suggest that an increased propensity toward global processing, indexed by greater MSE at coarser timescales, at baseline appears to facilitate eventual antidepressant treatment response.
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Project description:Previous research has noted that many factors greatly influence the spread of COVID-19. Contrary to explicit factors that are measurable, such as population density, number of medical staff, and the daily test rate, many factors are not directly observable, for instance, culture differences and attitudes toward the disease, which may introduce unobserved heterogeneity. Most contemporary COVID-19 related research has focused on modeling the relationship between explicitly measurable factors and the response variable of interest (such as the infection rate or the death rate). The infection rate is a commonly used metric for evaluating disease progression and a state's mitigation efforts. Because unobservable sources of heterogeneity cannot be measured directly, it is hard to incorporate them into the quantitative assessment and decision-making process. In this study, we propose new metrics to study a state's performance by adjusting the measurable county-level covariates and unobservable state-level heterogeneity through random effects. A hierarchical linear model (HLM) is postulated, and we calculate two model-based metrics-the standardized infection ratio (SDIR) and the adjusted infection rate (AIR). This analysis highlights certain time periods when the infection rate for a state was high while their SDIR was low and vice versa. We show that trends in these metrics can give insight into certain aspects of a state's performance. As each state continues to develop their individualized COVID-19 mitigation strategy and ultimately works to improve their performance, the SDIR and AIR may help supplement the crude infection rate metric to provide a more thorough understanding of a state's performance.
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Project description:Nestedness is a property of interaction networks widely observed in natural mutualistic communities, among other systems. A perfectly nested network is characterized by the peculiarity that the interactions of any node form a subset of the interactions of all nodes with higher degree. Despite a widespread interest on this pattern, no general consensus exists on how to measure it. Instead, several nestedness metrics, based on different but not necessarily independent properties of the networks, coexist in the literature, blurring the comparison between ecosystems. In this work, we present a detailed critical study of the behavior of six nestedness metrics and the variants of two of them. In order to evaluate their performance, we compare the obtained values of the nestedness of a large set of real networks among them and against a maximum-entropy and maximum-likelihood null model. We also analyze the dependencies of each metrics on different network parameters, as size, fill, and eccentricity. Our results point out, first, that the metrics do not rank networks universally in terms of their degree of nestedness. Furthermore, several metrics show significant dependencies on the network properties considered. The study of these dependencies allows us to understand some of the observed systematic shifts against the null model. Altogether, this paper intends to provide readers with a critical guide on how to measure nestedness patterns, by explaining the functioning of several metrics and disclosing their qualities and flaws. Besides, we also aim to extend the application of null models based on maximum entropy to the scarcely explored area of ecological networks. Finally, we provide a fully documented repository that allows constructing the null model and calculating the studied nestedness indexes. In addition, it provides the probability matrices to build the null model for a large dataset of more than 200 bipartite networks.
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Project description:ObjectiveHorizontal consolidation in the hospital industry has gained momentum in the United States despite concerns over rising costs and lower quality. Hospital systems frequently point to potential gains in interoperability and electronic exchange of patient information as consolidation benefits. We sought to assess whether hospitals in different health system structures varied in their interoperable data sharing.Materials and methodsWe created a cross-sectional national hospital sample from the 2014 AHA Annual Survey and 2015 IT Supplement. We combined the existing taxonomy of health system organizational forms and the ONC's functionality-based, technology-agnostic definition of interoperability. We used logistic regression models to assess the relationship between health systems' organizational forms and interoperability engagement, controlling for hospital characteristics.ResultsWe found that interoperability engagement varied greatly across hospitals in different health system structures, with facilities in more centralized health systems more likely to be interoperable. Hospitals in one system type, featuring centralized insurance product development but diverse service offerings across member organizations, had significantly higher odds of being engaged in interoperable data sharing in our multivariate regression results.DiscussionThe heterogeneity in health system interoperability engagement indicates that incentives to share data vary greatly across organizational strategies and structures. Our results suggest that horizontal consolidation in the hospital industry may not bring significant gains in interoperability progress unless that consolidation takes a specific business alignment form.ConclusionPolicymakers should be wary of claims that horizontal consolidation will lead to interoperability gains. Future research should explore the specific mechanisms that lead to greater interoperability in certain health system organizational structures.
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Project description:Crossbreeding is a process in which animals from different breeds are mated together. The animals produced will exhibit a combination of both additive and non-additive genetic improvement from parental breeds that increase heterozygosity and negate inbreeding depression. However, crossbreeding may also break up the unique and often beneficial gene combinations in parental breeds, possibly reducing performance potential as the benefits of heterosis depends on the type of crossbreeding systems used and heritability of the traits. This effect of crossbreeding, especially on the genome architecture, is still poorly understood with respect to 3-breed crossbreeding systems. Thus, this study examined variation in genomic ancestry estimations relative to pedigree-based estimations and correlated breed composition to key production and health traits. Two rotational crossbred populations, referenced as ProCROSS and Grazecross were assessed and totaled 607 crossbred cattle. ProCROSS is a product of rotational crossbreeding of Viking Red (VKR), Holstein (HOL), and Montbeliarde (MON). In contrast, Grazecross consists of Viking Red (VKR), Normande (NOR), and Jersey (JER). Both breeding programs were aimed at capitalizing on the positive effect of heterosis. The VKR is a marketing term for Swedish Red, Danish Red, and Finnish Ayrshire breed which complicated breed determination. Therefore, genomic breed composition estimates were compared using two different representations of VKR, one of which was based on parents used in the crossing system and a second based on genotypes from the ancestral breeds that comprise VKR. Variation of breed composition estimates were assessed between pedigree and genome-based predictions. Lastly, Genomic estimations were correlated with production and health traits by comparing extreme performance groups to identify the relationship between breed ancestry and performance. With the exception of the JER breed composition in Grazecross, all other estimates of the purebred contribution to the ProCROSS and Grazecross showed a significant difference in their genomic breed estimation when using the VKR ancestral versus the VKR parental reference populations for admixture analysis. These observations were expected given the different relationship of each VKR representation to the crossbred cattle. Further analysis showed that regardless of which VKR reference population was used, the degree of MON and HOL breed composition plays a significant role in milk and fat production in ProCROSS, while the degree of VKR and NOR ancestry were related to improved health performance in Grazecross. In all, identifying the most appropriate and informative animals to use as reference animals in admixture analysis is an important factor when interpreting results of relationship and population structure, but some degree of uncertainty exists when assessing the relationship of breed composition to phenotypic performance.
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Project description:Change is unavoidable for organizations just as it is in every sphere of life. Whatever the reasons are, organizations need to change, keeping in mind the end goal to survive and to be successful. Organizations operate in an environment where globalisation is the common expression of the phenomenon that is driving a great dynamism in the business environment across the world and no business is immune from the effects of this "globalisation". Competition, policymaking and advancement in technology exist on a day-to-day basis (Hatch, 2009) as well as opportunities are no longer localised within a nation, region or continent, every business is now competing with competitors all over the world. These forces are in constant change and affect a large number of organizations, which involves creating new strategies and policies in order for the organizations to survive and compete within the global business world and also to improve organizational performance but, there are also many challenges as well as the intensification of competition. The usage of technology decides the quality and number of products and services to be delivered. Organizational and national restrictive execution and improvement are controlled by the state and types of technology. Technology likewise impacts the living states of individual and groups in organizations and countries and the relationship between them. Technology is inclined to change, and the condition of technology have direct connection to the relationship between the business and worker. Technology, labour and capital are interconnected. The data presented in this article is very salient in this regard.
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