We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure no-reply@cambridge.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Statistical methodology for handling omitted variables is presented in a multilevel modeling framework. In many nonexperimental studies, the analyst may not have access to all requisite variables, and this omission may lead to biased estimates of model parameters. By exploiting the hierarchical nature of multilevel data, a battery of statistical tools are developed to test various forms of model misspecification as well as to obtain estimators that are robust to the presence of omitted variables. The methodology allows for tests of omitted effects at single and multiple levels. The paper also introduces intermediate-level tests; these are tests for omitted effects at a single level, regardless of the presence of omitted effects at a higher level. A simulation study shows, not surprisingly, that the omission of variables yields bias in both regression coefficients and variance components; it also suggests that omitted effects at lower levels may cause more severe bias than at higher levels. Important factors resulting in bias were found to be the level of an omitted variable, its effect size, and sample size. A real data study illustrates that an omitted variable at one level may yield biased estimators at any level and, in this study, one cannot obtain reliable estimates for school-level variables when omitted child effects exist. However, robust estimators may provide unbiased estimates for effects of interest even when the efficient estimators fail, and the one-degree-of-freedom test helps one to understand where the problem is located. It is argued that multilevel data typically contain rich information to deal with omitted variables, offering yet another appealing reason for the use of multilevel models in the social sciences.
Five different ability estimators—maximum likelihood [MLE (θ)], weighted likelihood [WLE (θ)], Bayesian modal [BME (θ)], expected a posteriori [EAP (θ)] and the standardized number-right score [Z (θ)]—were used as scores for conventional, multiple-choice tests. The bias, standard error and reliability of the five ability estimators were evaluated using Monte Carlo estimates of the unknown conditional means and variances of the estimators. The results indicated that ability estimates based on BME (θ), EAP (θ) or WLE (θ) were reasonably unbiased for the range of abilities corresponding to the difficulty of a test, and that their standard errors were relatively small. Also, they were as reliable as the old standby—the number-right score.
Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling—model disturbances, random coefficients, and future response outcomes—and provides a unified procedure for predicting them. These predictors are best linear unbiased and are commonly known via the acronym BLUP; they are optimal in the sense of minimizing mean square error and are Bayesian under a diffuse prior.
For parameter estimation purposes, a multilevel model can be written as a linear mixed-effects model. In this way, parameters of the many equations can be estimated simultaneously and hence efficiently. For prediction purposes, we show that it is more convenient to retain the multiple equation feature of multilevel models. In this way, the efficient BLUPs are easy to compute and retain their intuitively appealing recursive form. We also derive explicit equations for standard errors of these different types of predictors.
Prediction in multilevel modeling is important in a wide range of applications. To demonstrate the applicability of our results, this paper discusses prediction in the context of a study of school effectiveness.
When there exist omitted effects, measurement error, and/or simultaneity in multilevel models, explanatory variables may be correlated with random components, and standard estimation methods do not provide consistent estimates of model parameters. This paper introduces estimators that are consistent under such conditions. By employing generalized method of moments (GMM) estimation techniques in multilevel modeling, the authors present a series of estimators along a robust to efficient continuum. This continuum depends on the assumptions that the analyst makes regarding the extent of the correlated effects. It is shown that the GMM approach provides an overarching framework that encompasses well-known estimators such as fixed and random effects estimators and also provides more options. These GMM estimators can be expressed as instrumental variable (IV) estimators which enhances their interpretability. Moreover, by exploiting the hierarchical structure of the data, the current technique does not require additional variables unlike traditional IV methods. Further, statistical tests are developed to compare the different estimators. A simulation study examines the finite sample properties of the estimators and tests and confirms the theoretical order of the estimators with respect to their robustness and efficiency. It further shows that not only are regression coefficients biased, but variance components may be severely underestimated in the presence of correlated effects. Empirical standard errors are employed as they are less sensitive to correlated effects when compared to model-based standard errors. An example using student achievement data shows that GMM estimators can be effectively used in a search for the most efficient among unbiased estimators.
Carbapenem-resistant Enterobacterales (CRE) are an urgent threat to healthcare, but the epidemiology of these antimicrobial-resistant organisms may be evolving in some settings since the COVID-19 pandemic. An updated analysis of hospital-acquired CRE (HA-CRE) incidence in community hospitals is needed.
Methods:
We retrospectively analyzed data on HA-CRE cases and antimicrobial utilization (AU) from two community hospital networks, the Duke Infection Control Outreach Network (DICON) and the Duke Antimicrobial Stewardship Outreach Network (DASON) from January 2013 to June 2023. The zero-inflated negative binomial regression model was used owing to excess zeros.
Results:
126 HA-CRE cases from 36 hospitals were included in the longitudinal analysis. The pooled incidence of HA CRE was 0.69 per 100,000 patient days (95% confidence interval [95% CI], 0.57–0.82 HA-CRE rate significantly decreased over time before COVID-19 (rate ratio [RR], 0.94 [95% CI, 0.89–0.99]; p = 0.02), but there was a significant slope change indicating a trend increase in HA-CRE after COVID-19 (RR, 1.32 [95% CI, 1.06–1.66]; p = 0.01). In 21 hospitals participating in both DICON and DASON from January 2018 to June 2023, there was a correlation between HA-CRE rates and AU for CRE treatment (Spearman’s coefficient = 0.176; p < 0.01). Anti-CRE AU did not change over time, and there was no level or slope change after COVID.
Conclusions:
The incidence of HA-CRE decreased before COVID-19 in a network of community hospitals in the southeastern United States, but this trend was disrupted by the COVID-19 pandemic.
Growing evidence suggests that direct oral anticoagulants (DOACs) may be suitable for cerebral venous thrombosis (CVT). The optimal strategy regarding lead-in parenteral anticoagulation (PA) prior to DOAC is unknown.
Methods:
In this post hoc analysis of the retrospective ACTION-CVT study, we compared patients treated with DOACs as part of routine care: those given “very early” DOAC (no PA), “early” (<5 days PA) and “delayed” (5–21 days PA). We compared baseline characteristics and outcomes between the very early/early and delayed groups. The primary outcome was a composite of day-30 CVT recurrence/extension, new peripheral venous thromboembolism, cerebral edema and intracranial hemorrhage.
Results:
Of 231 patients, 11.7% had very early DOAC, 64.5% early (median [IQR] 2 [1–2] days) and 23.8% delayed (5 [5–6] days). More patients had severe clinical/radiological presentations in the delayed group; more patients had isolated headaches in the very early/early group. Outcomes were better in the very early/early groups (90-day modified Rankin Scale of 0–2; 94.3% vs. 83.9%). Primary outcome events were rare and did not differ significantly between groups (2.4% vs. 2.1% delayed; adjusted HR 1.49 [95%CI 0.17–13.11]).
Conclusions:
In this cohort of patients receiving DOAC for CVT as part of routine care, >75% had <5 days of PA. Those with very early/early initiation of DOAC had less severe clinical presentations. Low event rates and baseline differences between groups preclude conclusions about safety or effectiveness. Further prospective data will inform care.
In this work we consider the problem of optimizing a stellarator subject to hard constraints on the design variables and physics properties of the equilibrium. We survey current numerical methods for handling these constraints, and summarize a number of methods from the wider optimization community that have not been used extensively for stellarator optimization thus far. We demonstrate the utility of new methods of constrained optimization by optimizing a quasi-axisymmetric stellarator for favourable physics properties while preventing strong shaping of the plasma boundary, which can be difficult to create with external current sources.
The COVID-19 pandemic has presented youth and families with a broad spectrum of unique stressors. Given that adolescents are at increased risk for mental health and emotional difficulties, it is critical to explore family processes that confer resilience for youth in the face of stress. The current study investigated caregiver emotion regulation (ER) as a familial factor contributing to youth ER and risk for psychopathology following stressful life events. In a longitudinal sample of 224 youth (Mage = 12.65 years) and their caregivers, we examined whether caregiver and youth engagement in ER strategies early in the pandemic mediated the associations of pandemic-related stress with youth internalizing and externalizing symptoms six months later. Leveraging serial mediation analysis, we demonstrated that caregiver and youth rumination, but not expressive suppression or cognitive reappraisal, mediated the prospective associations of pandemic-related stress with youth internalizing and externalizing symptoms. Greater exposure to pandemic-related stressors was associated with greater caregiver rumination, which, in turn, related to greater rumination in youth, and higher levels of youth internalizing and externalizing symptoms thereafter. Family interventions that target caregiver ER, specifically rumination, may buffer against the consequences of stress on youth engagement in maladaptive ER strategies and risk for psychopathology.
GX is a code designed to solve the nonlinear gyrokinetic system for low-frequency turbulence in magnetized plasmas, particularly tokamaks and stellarators. In GX, our primary motivation and target is a fast gyrokinetic solver that can be used for fusion reactor design and optimization along with wide-ranging physics exploration. This has led to several code and algorithm design decisions, specifically chosen to prioritize time to solution. First, we have used a discretization algorithm that is pseudospectral in the entire phase space, including a Laguerre–Hermite pseudospectral formulation of velocity space, which allows for smooth interpolation between coarse gyrofluid-like resolutions and finer conventional gyrokinetic resolutions and efficient evaluation of a model collision operator. Additionally, we have built GX to natively target graphics processors (GPUs), which are among the fastest computational platforms available today. Finally, we have taken advantage of the reactor-relevant limit of small $\rho _*$ by using the radially local flux-tube approach. In this paper we present details about the gyrokinetic system and the numerical algorithms used in GX to solve the system. We then present several numerical benchmarks against established gyrokinetic codes in both tokamak and stellarator magnetic geometries to verify that GX correctly simulates gyrokinetic turbulence in the small $\rho _*$ limit. Moreover, we show that the convergence properties of the Laguerre–Hermite spectral velocity formulation are quite favourable for nonlinear problems of interest. Coupled with GPU acceleration, which we also investigate with scaling studies, this enables GX to be able to produce useful turbulence simulations in minutes on one (or a few) GPUs and higher fidelity results in a few hours using several GPUs. GX is open-source software that is ready for fusion reactor design studies.
Fear learning is a core component of conceptual models of how adverse experiences may influence psychopathology. Specifically, existing theories posit that childhood experiences involving childhood trauma are associated with altered fear learning processes, while experiences involving deprivation are not. Several studies have found altered fear acquisition in youth exposed to trauma, but not deprivation, although the specific patterns have varied across studies. The present study utilizes a longitudinal sample of children with variability in adversity experiences to examine associations among childhood trauma, fear learning, and psychopathology in youth.
Methods
The sample includes 170 youths aged 10–13 years (M = 11.56, s.d. = 0.47, 48.24% female). Children completed a fear conditioning task while skin conductance responses (SCR) were obtained, which included both acquisition and extinction. Childhood trauma and deprivation severity were measured using both parent and youth report. Symptoms of anxiety, externalizing problems, and post-traumatic stress disorder (PTSD) were assessed at baseline and again two-years later.
Results
Greater trauma-related experiences were associated with greater SCR to the threat cue (CS+) relative to the safety cue (CS−) in early fear acquisition, controlling for deprivation, age, and sex. Deprivation was unrelated to fear learning. Greater SCR to the threat cue during early acquisition was associated with increased PTSD symptoms over time controlling for baseline symptoms and mediated the relationship between trauma and prospective changes in PTSD symptoms.
Conclusions
Childhood trauma is associated with altered fear learning in youth, which may be one mechanism linking exposure to violence with the emergence of PTSD symptoms in adolescence.
The Korean Basketball League(KBL) holds an annual draft to allow teams to select new players, mostly graduates from the elite college basketball teams even though some are from high school teams. In sports games, many factors might influence the success of an athlete. In addition to possessing excellent physical and technical factors, success in a sports game is also influenced by remarkable psychological factors. Several studies reported that elite sports players can control their anxiety during competition, which may lead to better performance. In particular, the temperament and characteristics of players have been regarded as crucial determinants of the player’s performance and goal. In this regard, numerous studies suggest that personality is considered to be an important predictor of long-term success in professional sports
Objectives
Based on previous reports and studies, we hypothesized that physical status, temperament and characteristics, and neurocognitive functions of basketball players could predict the result of KBL draft selection. Especially, temperament and characteristics were associated with the result of KBL selection. The basketball performances including average scores and average rebound were associated with emotional perception and mental rotation.
Methods
We recruited the number of 44 college elite basketball players(KBL selection, n=17; Non-KBL selection, n=27), and the number of 35 age-matched healthy comparison subjects who major in sports education in college. All participants were assessed with the Temperament and Character Inventory(TCI), Sports Anxiety Scales(SAS), Beck Depression Inventory(BDI), Perceived Stress Scale (PSS-10), Trail Making Test(TMT), and Computerized Neuro-cognitive Test(CNT) for Emotional Perception and Mental Rotation.
Results
Current results showed that physical status, temperament and characteristics, and Neurocognitive functions of college basketball players could predict the KBL draft selection. Among temperament and characteristics, novelty seeking and reward dependence were associated with KBL draft selection. The basketball performances including average scores and average rebound were associated with emotional perception and mental rotation.
Conclusions
In order to be a good basketball player for a long time, it was confirmed that temperamental factors and Neurocognitive factors were very closely related. Furthermore, it is also judged that these results can be used as basic data to predict potential professional basketball players.
People experience various negative emotions when they encounter stressful events, and these negative emotions contribute to the onset of illnesses. These emotional responses are not limited to just one; a person can experience multiple emotions at once, and the primary emotional reactions can vary depending on the severity and duration of the illness or life events. This is reason why we created a self-report scale to assess short-term emotional responses, focusing on the current emotional state experienced subjectively by patients.
Objectives
The purpose of this study was to develop an affective response scale (ARS) and examine its validity and reliability.
Methods
We established clusters of affective via a literature review and developed preliminary items based on the structure. We conducted expert content validation to converge on the final items, followed by construct validity and reliability analyses.
Results
The research findings indicate that the Affective Response Scale was composed of three main dimensions: anxiety, anger, and depression. Content validity results confirmed the validity of most items. The scale developed in this study was found to be valid in both exploratory and confirmatory factor analyses, and it was identified to be stable and consistent through the analysis of the internal reliability.
Conclusions
These results indicate that the ARS is highly reliable and valid, and that it can be utilized as an effective measure of the patient’s emotion and its severity.
Accordingly, the Korean Medication Algorithm Project for Bipolar Disorder (KMAP-BP) working committee, composed of domestic experts, developed Korea’s first KMAP-BP in 2002 and later in 2006, 2010, and 2010. A revised version of KMAP-BP was announced every four years four times in 2014 and 2018.6-10). The treatment strategy considering the safety and tolerability of KMAP-BP 2022 was developed by collecting opinions from domestic bipolar disorder experts.
Objectives
Safety and tolerability of drugs are very important factors in the treatment of bipolar disorder. An expert opinion survey was conducted on treatment strategies in various special clinical situations, such as significant weight gain, characteristic drug side effects, low drug adherence, pregnant and reproductive women, and genetic counseling.
Methods
A written survey about treatment strategies related to safety and tolerability was prepared and focused on significant weight gain, characteristic drug side effects, low drug adherence, pregnant and reproductive women, and genetic counseling. Ninety-three experts of the review committee completed the survey.
Results
In the case of weight gain occurring during drug treatment, it was preferred to replace it with a drug that caused less weight gain, such as lamotrigine, aripiprazole, or ziprasidone. If there was a significant weight gain due to the treatment drug, it was preferred to intervene as soon as possible. In the case of hyperprolactinemia, it was selected to change the medication and discontinue it for benign rash caused by lamotrigine. In improving drug adherence, the preference for long-acting injections increased. Antipsychotics can be used with great caution in pregnant or reproductive women.
Conclusions
Treatment strategies in various clinical situations related to safety and tolerability in drug treatment for bipolar disorder were described. It is hoped that it will be useful in practical clinical situations.
Anxiety disorders are one of the most common mental disorders, yet only less than 20% of people with anxiety disorders receive adequate treatment. Digital interventions for anxiety disorders can potentially increase access to evidence-based treatment. However, there is no comprehensive meta-analysis study that covers all modalities of digital interventions and all anxiety disorders.
Objectives
A preliminary meta-analysis was conducted to examine the treatment efficacy of digital interventions [e.g., virtual reality (VR)-, mobile application-, internet-based interventions] for anxiety disorders and to identify potential moderators that may lead to better treatment outcomes.
Methods
We searched Embase, PubMed, PsycINFO, Web of Science, and the Cochrane Library for randomized controlled trials examining the therapeutic efficacy of digital interventions for individuals with anxiety disorders from database inception to April 18, 2023. Search keywords were developed by combining the PICOS framework and MeSH terms. Data screening and extraction adhered to PRISMA guidelines. We used a random-effects model with effect sizes expressed as Hedge’s g. The quality of the studies was assessed using the Revised Cochrane risk-of-bias tool for randomized trials (RoB 2). The study protocol was registered in PROSPERO on April 22, 2023 (CRD42023412139).
Results
A systematic literature search identified 19 studies with randomized controlled trials (21 comparisons; 1936 participants) with high overall heterogeneity (Q = 104.49; P < .001; I2 = 80.9%). Digital interventions reduced anxiety symptoms with medium to large effect sizes (g = 0.78; 95% CI: 0.55-1.02; P < .001), with interventions for specific phobia showing the largest effect size (n = 6; g = 1.22; 95% CI: 0.51-1.93; P < .001). VR-based interventions had a larger effect size (n = 6; g = 0.98; 95% CI: 0.39-1.57; P < .001) than mobile- or internet-based interventions, which had medium effect sizes. Meta-regression results exhibited that effect sizes of digital interventions were associated with the mean age of participants (β = 0.04; 95% CI: 0.02-0.06; P < .001).
Conclusions
The results of this study provide evidence for the efficacy of digital interventions for anxiety disorders. However, this also suggests that the degrees of effectiveness in reducing anxiety symptoms can be moderated by the specific diagnosis, the modalities of digital technologies, and mean age, implying that the application of digital interventions for anxiety disorders should be accompanied by personalized guidance.
Mental healthcare services that address a variety of primary complaints which are highly related to maladaptive personality traits among the general population are important to prevent developing psychiatric disorders.
Objectives
This study aimed to examine the effectiveness of a digital mental health service (named “Mindling”) that focuses on maladaptive personality traits in the general population.
Methods
Participants were recruited through a South Korean community website and screened for adults between the ages of 18 and 60 in terms of personality traits such as perfectionism, low self-esteem, social isolation, or anxiety. Participants were allocated to four intervention programs (Riggy, Pleaser, Shelly, and Jumpy) based on their screening results and were randomly assigned to digital treatment and waitlist groups. Each intervention program was conducted online for 10 weeks. The primary outcomes were all measured by self-report questionnaires; in addition to stress levels, each program included measures of perfectionism (Riggy), low self-esteem (Pleaser), loneliness (Shelly), and anxiety (Jumpy). The secondary outcomes included self-efficacy, depression, and other psychological states. All participants completed pre-treatment (baseline), intervention (week 5), and post-treatment (week 10) assessments, and the treatment group completed a separate follow-up assessment (week 14).
Results
In the treatment group, 70.05% of the participants completed the full course of the digital intervention. The mean scores for each primary outcome measure and some secondary outcome measures were significantly different between baseline and post-treatment in the treatment group for the Total, Riggy, Pleaser, Shelly, and Jumpy programs, but these differences were not observed in the waitlist group. In addition, mean differences between the treatment and waitlist groups at post-treatment assessment were significant for all primary outcome measures and some secondary outcome measures. Specifically, the levels of stress (Total program), perfectionism (Riggy), loneliness (Shelly), and anxiety (Jumpy) were significantly lower in the treatment group, while self-esteem (Pleaser) was higher. In addition, the mean differences between post-treatment and follow-up assessment data were not statistically significant for all primary outcome measures and nearly all secondary outcome measures.
Conclusions
This study validated the effectiveness of the digital intervention program targeting maladaptive personality traits and suggested its sustainable effects.
The process of deformation in clays is visualized as the combination of recoverable deformation resulting from bending and rotation of individual particles and irrecoverable deformation due to relative movement between adjacent particles at their points of contact. The relative movement between particles is treated as a rate process in which interparticle bonds are continually broken and reformed as the deformation proceeds. Accordingly, the rate of deformation is governed by the activation energy associated with the rupture of interparticle bonds. Thus, in terms of a rheological model, the fundamental element consists of a spring, representing the recoverable deformation, in series with a rate process dashpot representing the irrecoverable deformation.
Owing to the heterogeneous nature of the fabric of clay soils, i.e. varying particle size, shape, orientation, surface characteristics, etc., a wide range of activation energies, elastic stiffness, and other material properties is anticipated. This is accounted for by assuming a Gaussian distribution for the model properties. Thus, the complete rheological model postulated in this study consists of a combination of spring and dashpot elements covering the complete spectrum of model properties.
The response of the rheological model is analyzed for creep and constant strain-rate loading. The analysis is accomplished numerically using a digital computer since no closed form solution exists for the non-linear systems of equations that result from this model. Experimental data for a number of triaxial tests on clays under various conditions of loading are presented for comparison with the model behavior.
An animal enters the world with a set of highly specialized and firmly directed instincts which are correlated with pre-typified situations in the environment. Consequently it lives in a surrounding world, structured by its instinctual inheritance, which is specific to its own particular species and admitting only a limited range of variations within which life for it is possible. Man at birth is, compared to the non-human animal, an unfinished being, and his surrounding world partakes of his unfinished character. It is a world that must be fashioned then by man's own activity; he must make a human world for himself.
Structured processes to improve the quality and impact of clinical and translational research are a required element of the Clinical and Translational Sciences Awards (CTSA) program and are central to awardees’ strategic management efforts. Quality improvement is often assumed to be an ordinary consequence of evaluation programs, in which standardized metrics are tabulated and reported externally. Yet evaluation programs may not actually be very effective at driving quality improvement: required metrics may lack direct relevance; they lack incentive to improve on areas of relative strength; and the validity of inter-site comparability may be limited. In this article, we describe how we convened leaders at our CTSA hub in an iterative planning process to improve the quality of our CTSA program by intentionally focusing on how data collection activities can primarily advance continuous quality improvement (CQI) rather than strictly serve as evaluative tools. We describe our CQI process, which consists of three key components: (1) Logic models outlining goals and associated mechanisms; (2) relevant metrics to evaluate performance improvement opportunities; and (3) an interconnected and collaborative CQI framework that defines actions and timelines to enhance performance.
Background: After a transient ischemic attack (TIA) or minor stroke, the long-term risk of subsequent stroke is uncertain. Methods: Electronic databases were searched for observational studies reporting subsequent stroke during a minimum follow-up of 1 year in patients with TIA or minor stroke. Unpublished data on number of stroke events and exact person-time at risk contributed by all patients during discrete time intervals of follow-up were requested from the authors of included studies. This information was used to calculate the incidence of stroke in individual studies, and results across studies were pooled using random-effects meta-analysis. Results: Fifteen independent cohorts involving 129794 patients were included in the analysis. The pooled incidence rate of subsequent stroke per 100 person-years was 6.4 events in the first year and 2.0 events in the second through tenth years, with cumulative incidences of 14% at 5 years and 21% at 10 years. Based on 10 studies with information available on fatal stroke, the pooled case fatality rate of subsequent stroke was 9.5% (95% CI, 5.9 – 13.8). Conclusions: One in five patients is expected to experience a subsequent stroke within 10 years after a TIA or minor stroke, with every tenth patient expected to die from their subsequent stroke.