Proactive, that is, unsolicited, prosociality is a key component of our hyper-cooperation, which in turn has enabled the emergence of various uniquely human traits, including complex cognition, morality and cumulative culture and technology. However, the evolutionary foundation of the human prosocial sentiment remains poorly understood, largely because primate data from numerous, often incommensurable testing paradigms do not provide an adequate basis for formal tests of the various functional hypotheses. We therefore present the results of standardized prosociality experiments in 24 groups of 15 primate species, including humans. Extensive allomaternal care is by far the best predictor of interspecific variation in proactive prosociality. Proactive prosocial motivations therefore systematically arise whenever selection favours the evolution of cooperative breeding. Because the human data fit this general primate pattern, the adoption of cooperative breeding by our hominin ancestors also provides the most parsimonious explanation for the origin of human hyper-cooperation.

BackgroundIn the past decades, various frameworks, methods, indicators, and tools have been developed to assess the needs as well as to monitor and evaluate (needs assessment, monitoring and evaluation; “NaME”) health research capacity development (HRCD) activities. This systematic review gives an overview on NaME activities at the individual and organizational level in the past 10 years with a specific focus on methods, tools and instruments. Insight from this review might support researchers and stakeholders in systemizing future efforts in the HRCD field.MethodsA systematic literature search was conducted in PubMed and Google Scholar. Additionally, the personal bibliographies of the authors were scanned. Two researchers independently reviewed the identified abstracts for inclusion according to previously defined eligibility criteria. The included articles were analysed with a focus on both different HRCD activities as well as NaME efforts.ResultsInitially, the search revealed 700 records in PubMed, two additional records in Google Scholar, and 10 abstracts from the personal bibliographies of the authors. Finally, 42 studies were included and analysed in depth. Findings show that the NaME efforts in the field of HRCD are as complex and manifold as the concept of HRCD itself. NaME is predominately focused on outcome evaluation and mainly refers to the individual and team levels.ConclusionA substantial need for a coherent and transparent taxonomy of HRCD activities to maximize the benefits of future studies in the field was identified. A coherent overview of the tools used to monitor and evaluate HRCD activities is provided to inform further research in the field.

This article was migrated. The article was marked as recommended. The idea of this paper is to offer a blueprint, with that facilitators have a guide to set up a complete digital teaching scenario according to the latest insights of didactical research. The corona pandemic forced higher education institutions all around the world to radically shift their curricula from a mix of face-to-face and remote teaching methods to a fully remote curriculum. Though challenging, this time provides opportunities to implement new educational methods and improve the quality of digital teaching. The classical concept of the inverted classroom was modified to meet the special needs of online settings. The proposed online Inverted Classroom Model (oICM) includes the following phases: (1) pre-phase, (2) self-learning-phase, (3) Synchronous online face-to-face phase, (4) transfer-phase, (5) evaluation. Recommendations and potential tools are provided for each phase. The oICM is an innovative and easy to use approach to shape digital teaching and learning processes during and after the CoVid19 pandemic. This blueprint is developed by the committee "Digitalization" of the German Association for Medical Education (GMA) for facilitators without any prior experience with the ICM, but also for those who already teach in a traditional ICM.

Health risks are usually greater among GKV-insured children as compared with PKV-insured. Concerning morbidity, these differences can mainly be explained by differences in national origin and SES. Efforts aimed at reducing these health differences should therefore focus on risks associated with migration and low SES. In addition, differences concerning risk factors such as smoking could not be explained by differences in national origin and SES. Thus, there seems to be a general need for more preventive measures in the GKV (i.e., independent of national origin and SES).

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