Background Patients with chronic diseases commonly report fears of illness or symptoms recurring or worsening. These fears have been addressed from an illness-specific perspective (e.g., fear of cancer recurrence), a generic illness perspective (e.g., fear of progression), and a psychiatric perspective (DSM-5 illness anxiety disorder and somatic symptom disorder). The broader concept of health anxiety (HA) can also be applied to patients with a chronic disease. This review was conducted to investigate the conceptual, theoretical, measurementoverlap, and differences between these distinct perspectives. We also aimed to summarize prevalence, course, and correlates of these fears in different chronic illnesses.

Females tend to be more anxious than males while engaging in mathematics, which has been linked to lower math performance and higher math avoidance. A possible repercussion of this gender difference is the underrepresentation of females in STEM fields (science, technology, engineering, and math), as math competencies are an essential part of succeeding in such fields. A related, but distinct, area of research suggests that males tend to outperform females in tasks that require spatial processing (i.e., the ability to mentally visualize, rotate, and transform spatial and visual information). Interestingly, factors from the spatial processing domain (spatial ability and spatial anxiety) are important in explaining gender differences in math anxiety. Here, we examined three types of spatial anxiety and ability (imagery, navigation, and manipulation), as well as math ability, as mediators of gender differences in math anxiety. Undergraduate students (125 male; 286 female) completed assessments of their general level of anxiety, their math anxiety, and their spatial anxiety. They also completed a series of tasks measuring their mathematical skill, their spatial skills, and basic demographics. Results suggest that manipulation anxiety and ability, navigation anxiety, and math ability explained the gender difference in math anxiety, but manipulation anxiety was the strongest mediator of this relation. Conversely, all other measures did not explain the gender difference in math anxiety. These findings help us better understand the gender difference in mathematics, and this is important in reducing the gender gap in STEM fields.

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La modélisation paréquations structurelles avec MPlus (Caron, 2019) est un ouvrage de référence qui, divisé en trois parties, aborde les fonctions de base de Mplus (Partie 1 -Les rudiments), le traitement de données avec Mplus (Partie 2 -Traitement de données), ainsi que l'exécution d'un ensemble d'analyses statistiques impliquant de la modélisation paréquations structurelles en utilisant Mplus (Partie 3 -Les analyses). Reconnaissant l'utilisation croissante de la modélisation paréquations structurelles dans le domaine des sciences sociales, ainsi que le nombre limité de ressourceséducatives disponiblesà ce sujet, nous proposons un guide d'accompagnement pour la troisième partie du livre de Caron (2019), quiétendra son applicationà l'interface de programmation R. R est une interface de programmation libre d'accès et très versatile qui, similairementà Mplus, permet la réalisation d'analyses statistiques impliquant de la modélisation paŕ equations structurelles. L'objectif de cet article est de présenter la traduction en langage de programmation R des syntaxes de la troisième partie du livre de Caron (2019) ainsi que les résultats des sorties associées. Puisque cet article viseà servir de guide complémentaire au livre de Caron (2019), les bases théoriques sous-tendant les analyses statistiques qui y sont couvertes ainsi que l'interprétation des résultats issus de celles-ci ne font pas l'objet du présent article. Cet article couvre les analyses suivantes : la régression logistique, l'analyse de trajectoire, l'analyse factorielle exploratoire et confirmatoire, la médiation, la modération, la médiation modérée, l'analyse de classes latentes, l'analyse de modèles autorégressifs et autorégressifs croisés, l'analyse de trajectoire latente, et l'analyse de groupes multiples.