The gender gap in adolescent mental health: a cross-national investigation of 566,829 adolescents across 73 countries

Mental ill-health is a leading cause of disease burden worldwide. While women suffer from greater levels of mental health disorders, it remains unclear whether this gender gap differs systematically across regions and/or countries, or across the different dimensions of mental health. We analysed 2018 data from 566,827 adolescents across 73 countries for 4 mental health outcomes: psychological distress, life satisfaction, eudaemonia, and hedonia. We examine average gender differences and distributions for each of these outcomes as well as country-level associations between each outcome and purported determinants at the country level: wealth (GDP per capita), inequality (Gini index), and societal indicators of gender inequality (GII, GGGI, and GSNI). We report four main results: 1) The gender gap in mental health in adolescence is largely ubiquitous cross-culturally, with girls having worse average mental health; 2) There is considerable cross-national heterogeneity in the size of the gender gap, with the direction reversed in a minority of countries; 3) Higher GDP per capita is associated with worse average mental health and a larger gender gap across all mental health outcomes; and 4) more gender equal countries have larger gender gaps across all mental health outcomes. Taken together, our findings suggest that while the gender gap appears largely ubiquitous, its size differs considerably by region, country, and dimension of mental health. Findings point to the hitherto unrealised complex nature of gender disparities in mental health and possible incongruence between expectations and reality in high gender equal countries

A proteomic survival predictor for COVID-19 patients in intensive care

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care

A time-resolved proteomic and prognostic map of COVID-19

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease

How evolutionary behavioural sciences can help us understand behaviour in a pandemic

The COVID-19 pandemic has brought science into the public eye and to the attention of governments more than ever before. Much of this attention is on work in epidemiology, virology, and public health, with most behavioural advice in public health focussing squarely on ‘proximate’ determinants of behaviour. While epidemiological models are powerful tools to predict the spread of disease when human behaviour is stable, most do not incorporate behavioural change. The evolutionary basis of our preferences and the cultural evolutionary dynamics of our beliefs drive behavioural change, so understanding these evolutionary processes can help inform individual and government decisionmaking in the face of a pandemic

Measuring Competence in Supervisees and Supervisors : Satisfaction and Related Reactions in Supervision

Supervision is a highly valued component of practitioner training. This chapter discusses the following: factors influencing perceived satisfaction and alliance; and how satisfaction, alliance, and supervision relationships are currently measured; and reviews issues with the concept and its assessment. Given the importance of the supervisory relationship and of the supervisory alliance for the effectiveness of supervision and for the welfare of supervisees, the routine, repeated measurement of both these concepts, together with supervisee satisfaction, also assumes considerable utility. The chapter describes a selection of some commonly used measures: Supervisee Satisfaction Questionnaire (SSQ), Supervisory Relationship Questionnaire (SRQ), Supervisory Relationship Measure (SRM), Supervision Attitude Scale (SAS), Supervisory Working Alliance Inventory (SWAI), Supervisory Styles Inventory (SSI), Role Conflict and Ambiguity Inventory (RCAIC), and Evaluation Process within Supervision Inventory (EPSI)