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Based on a sample of 54 Israeli soldiers (51 % non-religious, 49 % religious) surveyed upon their return from combat, this study investigates the moderating role of religiosity as a factor that may strengthen cognitive processing tied to the belief in oneself to persevere (i.e., self-efficacy) after trauma and/or as a factor tied to enhanced external social support that religious individuals in particular may benefit from by their involvement in a religious community. Findings revealed (1) social support was tied to greater resilience within the general sample; (2) religious soldiers were less susceptible to traumatic stress than non-religious soldiers; and (3) religiosity moderated the relationship between self-efficacy and traumatic stress but not the relationship between social support and traumatic stress. Implications of findings are discussed.

Using a multilevel approach, this study examined the role of classroom emotional climate on students' academic achievement. Positive and negative emotions and homeroom teachers' support were used to assess the classroom emotional climate on the individual and class levels. To our knowledge, no study to date has investigated these specific aspects of the classroom emotional climate in relation to students' GPA. Data were collected from 73 classrooms in grades 7-12 (N = 1,641, students; 53% female) across three schools in Israel.Findings revealed that aggregated levels of both positive affect and perceived homeroom teacher support were positively tied to GPA and that aggregated levels of negative affect were negatively tied to GPA. The final model included gender, teacher support, individual and class emotions and explained 14% of within-class GPA. A central implication of this study is the relevance of having an emotionally supportive homeroom teacher for students' academic achievement.

The 'tend-and-befriend' approach (Taylor et al., ) posits that in times of stress, women in particular may tend to their loved ones and seek out social support as a coping mechanism. Two corollaries of this model are that when confronted with a situation of extreme stress, marriage may be more of a protective factor for men, as central beneficiaries of their wives 'tending' or nurturing response, and social support from a wider network may be more of a protective factor for women, as part of women's 'befriending' response to stress. Using a sample of 508 Israelis (M = 47 years; 48% women) under the real condition of a population under missile attacks, we investigated the latter two corollaries of the tend-and-befriend model, hypothesizing that marriage would buffer against symptoms of traumatic stress for men in particular and that social support would buffer against symptoms of traumatic stress for women in particular. Our findings revealed gender differences affirming both hypotheses and offering interpretive evidence in support of the tend-and-befriend model based on a gender-informed field study of responses to traumatic stress in real time. Copyright © 2015 John Wiley & Sons, Ltd.

The Impact Tech Startup (ITS) is a new, rapidly developing type of organizational category. Based on an entrepreneurial approach and technological foundations, ITSs adopt innovative strategies to tackle a variety of social and environmental challenges within a for-profit framework and are usually backed by private investment. This new organizational category is thus far not discussed in the academic literature. The paper first provides a conceptual framework for studying this organizational category, as a combination of aspects of social enterprises and startup businesses. It then proposes a machine learning (ML)-based algorithm to identify ITSs within startup databases. The UN’s Sustainable Development Goals (SDGs) are used as a referential framework for characterizing ITSs, with indicators relating to those 17 goals that qualify a startup for inclusion in the impact category. The paper concludes by discussing future research directions in studying ITSs as a distinct organizational category through the usage of the ML methodology.