Young adult caregivers experience reduced wellbeing when the combination of family care and an educational program becomes too demanding. We aim to clarify the role views, competences, and needs of lecturers regarding the identification and support of these students to prevent negative mental health consequences. A mixed-methods explanatory sequential design was used. We collected quantitative data using a survey of lecturers teaching in bachelor education programs in the Netherlands (n = 208) and then conducted in-depth interviews (n = 13). Descriptive statistics and deductive thematic analyses were performed. Most participants (70.2%) thought that supporting young adult caregivers was the responsibility of the educational institution, and 49% agreed that it was a responsibility of the lecturer, but only 66.8% indicated that they feel competent to do so. However, 45.2% indicated that they needed more training and expertise to identify and support these students. All interviewees felt responsible for their students\' wellbeing but highlighted a lack of clarity regarding their role fulfillment. In practice, their ability to identify and support these students depended on their available time and level of expertise. The lecturers required agreements on responsibility and procedures for further referral, as well as information on support and referral opportunities, communication skills courses, and peer-to-peer coaching.

The early career phase is a key period of identity maintenance and change. But, it is also ripe with important, attention-grabbing occurrences (i.e., critical events) that may modify these processes, particularly influencing women\'s leadership pursuit. Because previous research has overlooked if or how such events might alter identifying or if these processes differ for people who identify as men and women, we integrate the identity and critical events literatures to elaborate on how positive and negative critical events may shape men and women\'s identifying in the work- and non-work domains over time. We propose that critical events\' effects on identity salience will occur both within and across domains, but that these effects will be stronger within (vs. across) domains. While both positive and negative events can exert negative effects on subsequent identity salience, we propose that the effects of critical events on identity salience may be stronger for women (vs. men). Finally, we connect work identity salience with subsequent leadership status, including contextual moderators that enhance or undermine these effects (i.e., inclusive organizational climate and mega-threats, respectively). We conclude with theoretical and practical implications of this research, including for workforce efficiency and social sustainability. We also highlight calls for future research stemming from our review [e.g., sustainability critical events and gendered analyses for (more) accurate science] as well as fruitful research areas and innovative practices at the work-non-work interface for professionals on the path to leadership.

Perinatal anxiety affects an estimated 15% of women globally and is associated with poor maternal and infant outcomes. Identifying women with anxiety is essential to prevent these adverse associations, but there are a number of challenges around measurement. We used data from England\'s 2020 National Maternity Survey to compare the prevalence of anxiety symptoms at six months postpartum using three different measures: the two-item Generalised Anxiety Disorders Scale (GAD-2), the anxiety subscales of the Edinburgh Postnatal Depression Scale (EPDS-3A) and a direct question. The concordance between each pair of measures was calculated using two-by-two tables. Survey weights were applied to increase the representativeness of the sample and reduce the risk of non-response bias. The prevalence of postnatal anxiety among a total of 4611 women was 15.0% on the GAD-2, 28.8% on the EPDS-3A and 17.1% on the direct question. Concordance between measures ranged between 78.6% (95% CI 77.4-79.8; Kappa 0.40) and 85.2% (95% CI 84.1-86.2; Kappa 0.44). Antenatal anxiety was the strongest predictor of postnatal anxiety across all three measures. Women of Black, Asian or other minority ethnicity were less likely to report self-identified anxiety compared with women of White ethnicity (adjusted odds ratio 0.44; 95% CI 0.30-0.64). Despite some overlap, different anxiety measures identify different groups of women. Certain population characteristics such as women\'s ethnicity may determine which type of measure is most likely to identify women experiencing anxiety.

Thin-walled structures (TWS) were widely used in engineering equipment, and may be subjected to impact loads to produce different degrees of structural damage during application. However, it is a difficult problem to determine the impact load conditions for these structural damages. In this study, we developed a novel method of identifying the impact load condition of the thin-walled structure damage, which is based on particle swarm optimization-backpropagation (PSO-BP) neural network. First, the known impact position and velocity are applied to the finite element model (FEM) of the TWS to produce permanent plastic deformation, and to fit the characteristic shape of the deformation is needed by invoking the multivariate polynomial function. Then, the method is devoted to build a basic data set. With impact position and velocity as input and function coefficients as output, a model of extended PSO-BP neural network is established. Besides, the basic sample set is expanded to solve the lack of samples. Ultimately, utilizing the expanded total sample set as training data, function coefficients, impact position and velocity will be outputted. On the basis of the known functional coefficients of deformed surfaces, a model of predictive PSO-BP neural network is established and predicted. Furthermore, we predicted the collision position and velocity using a conventional BP neural network in the same way. Finally, the predicted impact position and velocity is compared with the analysis results of the FEM, which verifies that the PSO-BP neural network algorithm has high accuracy.

Primary school teachers should be able to identify struggling learners who may have dyslexia type learning difficulties, in order to facilitate early intervention. Considering this importance, a nationwide survey was conducted in Sri Lanka with 705 primary school teachers among randomly selected schools in order to investigate teacher readiness to identify learners with dyslexia. Teacher readiness was measured based on three variables (a) teachers\' self-reported basic knowledge of dyslexia, (b) their self-reported awareness of local tools and processes used to identify dyslexia and (c) their self-reported attitudes towards engaging in identifying dyslexia. Data were gathered through a structured questionnaire. Logistic regression analyses revealed that the participants had minimal readiness to engage in identifying learners with dyslexia. However, most of them showed positive attitudes towards actively engaging in identifying dyslexia.