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The current study analysed the relationships between socio-economic status, migration background, and non-cognitive factors and PISA reading and mathematics achievement. The results from multi-level analyses on Sweden's PISA survey from 2018 indicate that both mathematics achievement and reading achievement are affected by SES, migration background, reading abilities, growth mindset, and the ability to master the content. Between-school level differences are explained by reading motivation and the mother's educational level. Our findings stress the importance of both socio-demographic, socio-economic, and non-cognitive factors such as reading self-concept and growth mindset for both mathematics achievement and reading achievement.

Abstract Background and aim Common mental disorders are common reasons for long-term sick leave, especially among women. Return to work is often complex and unsuccessful, why more knowledge is needed regarding women’s health and psychological well-being in the return-to-work process. Therefore, the aim was to describe women’s health and psychological well-being in the return-to-work process, from women’s and first-line managers’ perspectives. Methods Individual interviews were conducted with 17 women and 16 first-line managers. Qualitative content analysis was performed based on the content areas “Women’s health (i.e. overall well-being, both physical and psychological) throughout the whole RTW process” and “Women’s psychological well-being (happiness, meaning and a sense of being significant) at work after work resumption” Themes and categories were created. Results Women and managers had similar descriptions, i.e. that women’s health and psychological well-being depend on the individual characteristics of women themselves, their private life, work and other stakeholders. However, women described relational work tasks (e.g. meeting patients) as beneficial for health, and highlighted small stressors in the work environment, which the managers did not. Having work that was compatible with private life, being in good health, having stimulating work tasks and strengthening relationships at work were important for the women’s psychological well-being. Conclusions Based on women’s and first-line managers experiences, promotion of women’s health and psychological well-being during the return-to-work process requires individually adapted assessments and actions involving women’s entire life situation. First-line managers should know that relational work tasks (e.g., meeting patients) can be beneficial for women’s health as well as that minor stressor in the work environment can put their health at risk.

Abstract Background While psychological treatments are effective, a substantial portion of patients do not benefit enough. Early identification of those may allow for adaptive treatment strategies and improved outcomes. We aimed to evaluate the clinical usefulness of machine-learning (ML) models predicting outcomes in Internet-based Cognitive Behavioural Therapy, to compare ML-related methodological choices, and guide future use of these. Methods Eighty main models were compared. Baseline variables, weekly symptoms, and treatment activity were used to predict treatment outcomes in a dataset of 6695 patients from regular care. Results We show that the best models use handpicked predictors and impute missing data. No ML algorithm shows clear superiority. They have a mean balanced accuracy of 78.1% at treatment week four, closely matched by regression (77.8%). Conclusions ML surpasses the benchmark for clinical usefulness (67%). Advanced and simple models perform equally, indicating a need for more data or smarter methodological designs to confirm advantages of ML.

Abstract Background Concern over medical students’ well-being is a global issue, with studies showing high psychological distress rates. Impostor Phenomenon (IP), i.e., underestimating one’s abilities, attributing success to external factors, and feeling like a fraud, has been implicated as one reason behind these troubling findings. Meanwhile, resilience has been suggested to protect against psychological distress. This study aimed to investigate the prevalence of IP and its association with resilience among undergraduate medical students. Methods The Clance Impostor Phenomenon Scale (CIPS), the Brief Resilience Scale (BRS), and sociodemographic questions were completed by 457 medical students registered in their 2-10th semester at a Swedish university. Of the respondents, 62.6% identified as women, 36.1% as men, and 1.3% as others. Results The prevalence of IP was 58.4% (defined as CIPS score ≥ 62). According to the CIPS scoring guidelines, 10.3% of participants had low IP, 29.5% moderate, 41.6% frequent, and 18.6% intense IP. Of all participants, almost 90% experienced at least moderate and 60.2% frequent to intense IP. Women had significantly higher CIPS scores and lower BRS scores than men. In contrast, neither attending semester nor age group significantly impacted CIPS scores. Finally, there was a moderate inverse correlation between the level of resilience and the prevalence of IP. Conclusions Our findings suggest that the majority of Swedish medical students feels like an impostor, and of these students, most do so frequently. Furthermore, IP is more prevalent among female students – who also show lower levels of resilience. Moreover, our results indicated that IP could hinder achieving higher resilience. Future longitudinal studies should investigate how gender norms may contribute to IP feelings and explore the potential advantages of counteracting IP and strengthening resilience among medical students. However, targeted interventions addressing individual students’ IP and resilience are insufficient. There is also a need to address structural aspects of the educational environment, such as gender stereotypes, that may contribute to IP.

The next generation of central receivers is expected to reach high outlet temperatures of 800 °C and above, maintain stable operation and react to a varying incoming flux magnitude caused by hourly and seasonal variations, endure high-temperature operation, and remain cost-effective. Particle-based central receivers are being considered due to their high-temperature durability and favorable thermal properties of particle materials such as bauxite particles. This paper describes a new particle-based central receiver concept and provides an initial exploration of its performance in comparison with the existing CentRec® technology. Discrete Element Method (DEM) simulations demonstrated that a stable falling and sliding particle film can be achieved inside the SlideRec. The residence time of particle flow within the SlideRec is estimated to be higher than a falling particle receiver and similar to that of an obstructed-flow receiver. The results of the thermal model (incorporating reflective, radiative, convective and conductive heat losses) indicate that the SlideRec demonstrates a higher thermal efficiency than the CentRec® under an incoming aperture flux of between 0.1 MW/m2 and 1.7 MW/m2. The concept therefore is promising and is recommended for further experimental exploration.

The extent to which people with intellectual disabilities (ID) are voting is an under-researched area. In order to fill this gap, we conducted a systematic literature review with a focus on voter turnout and democratic inclusiveness among people with ID. In total, we found N = 9 studies that met our inclusion criteria, all of which were based on samples from Western countries such as Croatia, Ireland, Netherlands, Sweden, Switzerland, UK and US. Our findings suggest that people with ID have substantially lower voter turnout than ordinary voters. Constraining factors for voting and other instances of democratic inclusion include educational limitations and physical obstacles at ballot places, as well as being under full guardianship. On the other hand, living in a household with a voter constitutes a facilitating factor. Because of the limited number of studies, we suggest that more basic quantitative and qualitative research in many countries ought to be conducted.

Background Optimising maintenance drug treatment selection for people with bipolar disorder is challenging. There is some evidence that clinical and demographic features may predict response to lithium. However, attempts to personalise treatment choice have been limited. Method We aimed to determine if machine learning methods applied to electronic health records could predict differential response to lithium or olanzapine. From electronic United Kingdom primary care records, we extracted a cohort of individuals prescribed either lithium (19,106 individuals) or olanzapine (12,412) monotherapy. Machine learning models were used to predict successful monotherapy maintenance treatment, using 113 clinical and demographic variables, 8,017 (41.96%) lithium responders and 3,831 (30.87%) olanzapine responders. Results We found a quantitative structural difference in that lithium maintenance responders were weakly predictable in our holdout sample, consisting of the 5% of patients with the most recent exposure. Age at first diagnosis, age at first treatment and the time between these were the most important variables in all models. Discussion Even if we failed to predict successful monotherapy olanzapine treatment, and so to definitively separate lithium vs. olanzapine responders, the characterization of the two groups may be used for classification by proxy. This can, in turn, be useful for establishing maintenance therapy. The further exploration of machine learning methods on EHR data for drug treatment selection could in the future play a role for clinical decision support. Signals in the data encourage further experiments with larger datasets to definitively separate lithium vs. olanzapine responders.

While well-being does generally constitute a moderate predictor of school achievement, research on the predictive validity of cognitive ability for well-being in school contexts remains scant. The current study analyzed longitudinal relations between cognitive ability measured at age 13 (Grade 6) and well-being measured at age 18 (Grade 12, valid N = 2,705) in a Swedish sample, using several multivariate model techniques. The results indicate that cognitive ability was not a statistically significant predictor when several predictors were entered in a multiple regression model. However, gender was a significant covariate as girls and young women have a substantially lower degree of self-reported well-being. This casts light on the limitations of cognitive ability as a construct for some non-cognitive outcomes, at least in shorter and narrower spatial–temporal contexts.

Finite element analysis (FEA) is a computational method used to predict the behaviour (stresses, strains and deformation) of a structure under predefined loading conditions. It can be applied to different biological structures, such as bone, to study defined muscle-driven scenarios. However, as muscle is an extremely complex structure to model, evolutionary biologists usually model muscle forces indirectly. In 2007, the BONELOAD MATLAB routine was developed to distribute muscle forces on a surface defined by the user. This routine then had to be coupled with a pre-existing FEA software (e.g. Strand7) to perform the analyses and has been widely used ever since. In this manuscript, we present a new method to run muscle-driven finite element simulations on a bone by distributing muscle forces on their insertions area, all within a single environment. We apply this protocol in three different situations: two biting simulations (unilateral and bilateral) and a shoulder flexion simulation. We demonstrate how to prepare the mesh, delineate the muscle origins and insertions, define the constraints, adjust material properties, choose a loading scenario (uniform, tangential or tangential-plus-normal), and extract the results. Our automated script meshes the 3D model, defines the constraints and distributes muscle forces within a single simulation software: ‘Metafor’ (nonlinear solver, owned and distributed by Gesval S.A) or ‘Fossils’ (a new open-source linear static solver developed in the frame of this work). ‘Metafor’ and ‘Fossils’ can perform the entire protocol (from the meshing to the muscle-induced simulations) on high-resolution volumetric meshes (millions of tetrahedra) and rapidly, exceeding the processing time of other widely used software protocols by up to four times. We demonstrate that the results obtained from our protocol are highly congruent with brands such as Strand7. Thus, our protocol opens up the possibility to routinely and rapidly simulate the behaviour of high-precision muscle-driven FE models containing millions of tetrahedra.

It is well established that socioeconomic status, cognitive ability, and non-cognitive abilities such as self-efficacy are substantially associated with academic achievement. However, the specific relationships of these variables remain a relatively unexplored topic in regard to more recent and representative samples. The current study examined such relations by taking advantage of two cohorts (total N = 12,315) of Swedish students at the elementary (Grade 6) and lower-secondary school levels (Grade 8) in the compulsory school. The regression models showed that all three variables explained a substantial portion of grade variance, with cognitive ability having the strongest relationship, followed by non-cognitive abilities, and SES. Longitudinal associations, which accounted for previous academic achievement, showed that the three variables did still explain a substantial amount of grade variance.