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Most women with epilepsy (WWE) will experience stable seizure control during pregnancy. Adverse fetal outcomes with epilepsy include spontaneous abortion, preterm birth, fetal growth restriction, major congenital malformation (MCM), hypertensive disorders of pregnancy, postpartum hemorrhage, peripartum depression, and—rarely—maternal death. Studies reporting these increased risks may be biased by differences in preexisting medical conditions, other patient characteristics, and anti-seizure medication (ASM) use and type. Poor seizure control preceding pregnancy, unplanned pregnancy, and polytherapy are associated with higher risks. Antenatal care should be coordinated by an experienced multidisciplinary team. Monotherapy with an appropriate ASM at the lowest effective dose is the goal, and drug levels should be monitored. Second trimester fetal anatomical sonography is the best screening modality for neural tube defects and other MCMs. Serial third trimester fetal growth ultrasounds are recommended. WWE are likely to have an uncomplicated labour and delivery. Epilepsy is not an indication for induction of labour or caesarean delivery. The risk of intrapartum seizures is 2−3%, and intractable seizures necessitating urgent delivery are rare. Attention is needed to avoid dehydration, missed ASM doses, sleep deprivation, and pain during labour and postpartum. WWE should be screened and counselled regarding their heightened risk of peripartum depression.
We propose that historical myths fall into two distinctive categories: Traumatic and cooperative. Traumatic myths, highlighting collective suffering, can undermine trust and foster conspiracy theories, whereas cooperative myths, emphasizing collective action, enhance group cohesion and within-group coalition building. Psychological and sociological evidence supports these divergent impacts of historical myths both in nations and social movements.
There are widespread assumptions that implicit group bias leads to biased behavior. This chapter summarizes existing evidence on the link between implicit group bias and biased behavior, with an analysis of the strength of that evidence for causality. Our review leads to the conclusion that although there is substantial evidence that implicit group bias is related to biased behavior, claims about causality are not currently supported. With plausible alternative explanations for observed associations, as well as the possibility of reverse causation, scientists and policy makers need to be careful about claims made and actions taken to address discrimination, based on the assumption that implicit bias is the problem.
This comment seeks to extend the authors' argument by considering how perceived fitness interdependence is generated in different settings. Based primarily on research from political science, it argues that strategic agents may seek to design myths that emphasize not only the longevity of their coalitions, but also internal features such as material and status equality and institutional impartiality.
Chapter 5 explores the logic of UN mediation as an ‘art’, which emphasises the fluid, contingent nature of mediation and prioritises relationships with negotiating parties. This chapter examines two core practices: emotional labour and discretion. The first section describes how UN mediators engage in emotional regulation to facilitate negotiations. The creation of emotional ties relies upon empathy and bonding in informal settings, which creates masculinised spaces that women have trouble accessing. In this case, the practice of empathy can be exclusionary. The second section examines how discretion – the choices mediators make about how to implement their mandates – is a key practice in UN mediation. How a mediator exercises their discretion is tied to their sense of political judgement. As such, using discretion unwisely can affect others' perceptions of a UN mediator's judgement. As WPS, especially the participation of local women, is often framed as showing partiality to one party over others, mediators are reluctant to use their discretion to advance the WPS Agenda. Instead, it is framed as a risk to the mediator's reputation for good political judgement and impartiality.
In this chapter, we examine the public’s understanding of implicit bias, a topic that has only recently caught the public’s attention. Given that political elites often set the contours of debate on political issues, we begin by conducting a systematic content analysis of newspaper headlines and cable news transcripts to assess the prevalence and nature of media coverage of implicit bias. We find that partisan media utilize starkly different frames regarding the scientific validity of the concept of implicit bias, the political intentions of those who use the phrase, and the requisite political recourses (if any). We then utilize two individual-level datasets to examine the mass public’s understanding of implicit bias. An original survey reveals a stark gulf in partisan understandings of implicit bias and an analysis of Project Implicit data highlights the interplay between personalized feedback from the IAT and ideology in shaping evaluations of the IAT. We conclude with a discussion of the challenges of science communication, particularly on issues relating to race, in a politically polarized age.
This enthusiastic introduction to the fundamentals of information theory builds from classical Shannon theory through to modern applications in statistical learning, equipping students with a uniquely well-rounded and rigorous foundation for further study. The book introduces core topics such as data compression, channel coding, and rate-distortion theory using a unique finite blocklength approach. With over 210 end-of-part exercises and numerous examples, students are introduced to contemporary applications in statistics, machine learning, and modern communication theory. This textbook presents information-theoretic methods with applications in statistical learning and computer science, such as f-divergences, PAC-Bayes and variational principle, Kolmogorov’s metric entropy, strong data-processing inequalities, and entropic upper bounds for statistical estimation. Accompanied by additional stand-alone chapters on more specialized topics in information theory, this is the ideal introductory textbook for senior undergraduate and graduate students in electrical engineering, statistics, and computer science.
The chapter focuses on Germany’s international relations, the development of the German army and military policy, the domestic consequences of military policy, and the origins of war in 1914.
It is very important to choose appropriate variables to be analyzed in multivariate analysis when there are many observed variables such as those in a questionnaire. What is actually done in scale construction with factor analysis is nothing but variable selection.
In this paper, we take several goodness-of-fit statistics as measures of variable selection and develop backward elimination and forward selection procedures in exploratory factor analysis. Once factor analysis is done for a certain number p of observed variables (the p-variable model is labeled the current model), simple formulas for predicted fit measures such as chi-square, GFI, CFI, IFI and RMSEA, developed in the field of the structural equation modeling, are provided for all models obtained by adding an external variable (so that the number of variables is p + 1) and for those by deleting an internal variable (so that the number is p − 1), provided that the number of factors is held constant.
A program SEFA (Stepwise variable selection in Exploratory Factor Analysis) is developed to actually obtain a list of the fit measures for all such models. The list is very useful in determining which variable should be dropped from the current model to improve the fit of the current model. It is also useful in finding a suitable variable that may be added to the current model. A model with more appropriate variables makes more stable inference in general.
The criteria traditionally often used for variable selection is magnitude of communalities. This criteria gives a different choice of variables and does not improve fit of the model in most cases.
The current study examined the comprehension and production of classifiers, case marking, and morphological passive structures among 414 child Japanese heritage speakers (mean age = 10.01 years; range = 4.02 – 18.18). Focusing on individual differences, we extracted latent experiential factors via the Q-BEx questionnaire (De Cat, Kašćelan, Prévost, Serratrice, Tuller, Unsworth, & The Q.-Be Consortium, 2022), which were then used to predict knowledge and use of these grammatical structures. The findings reveal that: (i) experiential factors such as heritage language (HL) engagement at home and within the community modulate grammatical performance differentially from childhood through adolescence, and (ii) HL proficiency, immersion experiences, and literacy systematically predict HL grammatical outcomes. These results indicate that particular language background factors hold differential significance at distinct developmental stages and that higher proficiency, richer immersion experiences, and literacy engagement in the HL are crucial for the development of core grammatical structures.
We investigate under what conditions the matrix of factor loadings from the factor analysis model with equal unique variances will give a good approximation to the matrix of factor loadings from the regular factor analysis model. We show that the two models will give similar matrices of factor loadings if Schneeweiss' condition, that the difference between the largest and the smallest value of unique variances is small relative to the sizes of the column sums of squared factor loadings, holds. Furthermore, we generalize our results and discus the conditions under which the matrix of factor loadings from the regular factor analysis model will be well approximated by the matrix of factor loadings from Jöreskog's image factor analysis model. Especially, we discuss Guttman's condition (i.e., the number of variables increases without limit) for the two models to agree, in relation with the condition we have shown, and conclude that Schneeweiss' condition is a generalization of Guttman's condition. Some implications for practice are discussed.