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Abstract Trauma scientists have raised the alarm about the devastating consequences of the Ukraine war on mental health. We examined how higher education students—as indirect victims—coped with this conflict and how they emotionally reacted during 2022. We involved 2314 students from 16 countries through an online survey. A structural equation model indicated significant relations between war-related worry about military and macroeconomics domains and two coping strategies (opposition, support giving), in turn significantly linked with six emotions. The model was strongly invariant across gender, study field, and geographic area. The most frequent emotions were anger and anxiety, followed by two future-centred emotions (hopelessness and hope). Emotions were more frequent for females and students of the countries geographically close to the war region. Our findings call for evidence-based policy recommendations to be implemented by institutions to combat the negative short and long-term psychological sequelae of being witnesses of armed conflicts.

Abstract Pressures and responsibilities of medical school put a strain on medical student's personal wellbeing, leading among all to high rates of anxiety, emotional discomfort and stress. In this work we evaluated the effectiveness of a comprehensive Mindfulness-Based Intervention (MBI) in reducing this load. The intervention comprised 10 twice-a-week Integral Meditation classes, dietary advice, and brief yoga sessions. We performed a randomized trial on two cohort of medical students from Italian universities: 239 in cohort 1 (106 treated and 133 controls), and 123 in cohort 2 (68 treated and 55 control) for a total sample of 362 students. Nine questionnaires for evaluating the effectiveness of our intervention on stress (PSS), state anxiety (STAIX-1), well-being (WEMWBS), mind-wandering (MW-S), overall distress (PANAS), emotion regulation (DERS), resilience (RS-14), and attentional control (ACS-C and ACS-D) were collected both pre and post intervention. Linear mixed effect models were run on the whole sample showing that, after multiple testing correction, our intervention was effective in reducing perceived stress (β = − 2.57 [− 4.02; − 1.12], p = 0.004), improving mental well-being (β = 2.82 [1.02; 4.63], p = 0.008) and emotional regulation (β = − 8.24 [− 12.98; − 3.51], p = 0.004), resilience (β = 3.79 [1.32; 6.26], p = 0.008), reducing the tendency to wander with the mind (β = − 0.70 [− 0.99; − 0.39], p = 0.0001), ameliorating the ability to maintain attention (AC-S (β = − 0.23 [− 0.44; − 0.02], p = 0.04) and AC-D (β = − 0.19 [− 0.36; − 0.01], p = 0.04)), and the overall distress (β = 1.84 [0.45; 3.23], p = 0.02).

Abstract COVID-19 pandemic had a negative impact on the mental health and well-being (WB) of citizens. This cross-sectional study included 4 waves of data collection aimed at identifying profiles of individuals with different levels of WB. The study included a representative stratified sample of 10,013 respondents in Italy. The WHO 5-item well-being scale (WHO-5) was used for the assessment of WB. Different supervised machine learning approaches (multinomial logistic regression, partial least-square discriminant analysis—PLS-DA—, classification tree—CT—) were applied to identify individual characteristics with different WB scores, first in waves 1–2 and, subsequently, in waves 3 and 4. Forty-one percent of participants reported “Good WB”, 30% “Poor WB”, and 28% “Depression”. Findings carried out using multinomial logistic regression show that Resilience was the most important variable able for discriminating the WB across all waves. Through the PLS-DA, Increased Unhealthy Behaviours proved to be the more important feature in the first two waves, while Financial Situation gained most relevance in the last two. COVID-19 Perceived Risk was relevant, but less than the other variables, across all waves. Interestingly, using the CT we were able to establish a cut-off for Resilience (equal to 4.5) that discriminated good WB with a probability of 65% in wave 4. Concluding, we found that COVID-19 had negative implications for WB. Governments should support evidence-based strategies considering factors that influence WB (i.e., Resilience, Perceived Risk, Healthy Behaviours, and Financial Situation).