This meta-analytic review examines the role of optimism, social support, and coping strategies in contributing to posttraumatic growth. Results from 103 studies showed that all three systems of variables yielded significant effect sizes. Religious coping and positive reappraisal coping produced the largest effect sizes. Social support, seeking social support coping, spirituality, and optimism were moderately related to posttraumatic growth. Acceptance coping yielded the smallest effect sizes. Moderator analyses showed that effect sizes did not differ according to time elapsed since trauma, gender, and type of posttraumatic growth measure (posttraumatic growth vs. benefit finding). Age and gender were significant moderators of religious coping, whereas study design (longitudinal vs. cross-sectional) significantly moderated the effect of positive reappraisal coping. Implications for research and interventions on posttraumatic growth are also discussed.

Lockdowns to control the spread of the coronavirus disease 2019 (COVID-19) have had profound effects on everyday life worldwide, but their effect on mental health remains unclear because available meta-analyses and reviews rely mostly on cross-sectional studies. We conducted a rapid review and meta-analysis of longitudinal studies and natural experiments investigating the relationship between COVID-19 lockdowns and mental health. A total of 25 studies involving 72 004 participants and 58 effect sizes were analyzed. Using a random effects model, we found that lockdowns had small effects on mental health symptoms, g = 0.17, s.e. = 0.05, 95% CI (0.06–0.24), p = 0.001, but the effects on positive psychological functioning, g = −0.12, s.e. = 0.11, 95% CI (−0.33 to 0.09), p = 0.27, were not significant. Multivariate analysis of effect sizes revealed significant and relatively small effect sizes for anxiety and depression, while those for social support, loneliness, general distress, negative affect, and suicide risk were not significant. The results indicated substantial heterogeneity among studies, but meta-regression analyses found no significant moderation effects for mean age, gender, continent, COVID-19 death rate, days of lockdown, publication status or study design. The psychological impact of COVID-19 lockdowns is small in magnitude and highly heterogeneous, suggesting that lockdowns do not have uniformly detrimental effects on mental health and that most people are psychologically resilient to their effects.

Background. To investigate the relationship between risk perception, worry, control, trust, exposure to an educational campaign, media exaggeration with recommendations for pandemic influenza H1N1 2009. Methods. Cross sectional telephone survey using random digit dialing. A total of 1010 adult Italians were interviewed by telephone between 16 and 19 February 2010. The survey instrument included demographic data, measures on risk perception, worry, trust and compliance with recommendations for pandemic influenza H1N1 2009. Results. Controlling for socio-demographic variables, compliance with all the recommended behaviors was associated with media trust, trust in the Ministry of Health, worry and perceived severity of illness. Perceptions that the risk of catching pandemic influenza H1N1 2009 is high, that the authorities are acting in the public's best interest in dealing with it, that the media had exaggerated the risks of catching it and that people can control their risk of catching it were associated with compliance with some recommended behaviors even after considering effects of socio-demographic characteristics. Conclusion. The results underscore the importance of building public trust and to consider the influence of risk perception and affective response in promoting compliance with recommended behaviors.

To investigate the factors predicting severity of bicycle crashes in Italy, we used an observational study of official statistics. We applied two of the most widely used data mining techniques, CHAID decision tree technique and Bayesian network analysis. We used data provided by the Italian National Institute of Statistics on road crashes that occurred on the Italian road network during the period ranging from 2011 to 2013. In the present study, the dataset contains information about road crashes occurred on the Italian road network during the period ranging from 2011 to 2013. We extracted 49,621 road accidents where at least one cyclist was injured or killed from the original database that comprised a total of 575,093 road accidents. CHAID decision tree technique was employed to establish the relationship between severity of bicycle crashes and factors related to crash characteristics (type of collision and opponent vehicle), infrastructure characteristics (type of carriageway, road type, road signage, pavement type, and type of road segment), cyclists (gender and age), and environmental factors (time of the day, day of the week, month, pavement condition, and weather). CHAID analysis revealed that the most important predictors were, in decreasing order of importance, road type (0.30), crash type (0.24), age of cyclist (0.19), road signage (0.08), gender of cyclist (0.07), type of opponent vehicle (0.05), month (0.04), and type of road segment (0.02). These eight most important predictors of the severity of bicycle crashes were included as predictors of the target (i.e., severity of bicycle crashes) in Bayesian network analysis. Bayesian network analysis identified crash type (0.31), road type (0.19), and type of opponent vehicle (0.18) as the most important predictors of severity of bicycle crashes.