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Data-driven platforms with rich data and learning analytics applications provide immense opportunities to support collaborative learning such as algorithmic group formation systems based on learning logs. However, teachers can still get overwhelmed since they have to manually set the parameters to create groups and it takes time to understand the meaning of each indicator. Therefore, it is imperative to explore predictive indicators for algorithmic group formation to release teachers from the dilemma with explainable group formation indicators and recommended settings based on group work purposes. Employing learning logs of group work from a reading-based university course, this study examines how learner indicators from different dimensions before the group work connect to the subsequent group work processes and consequences attributes through correlation analysis. Results find that the reading engagement and previous peer ratings can reveal individual achievement of the group work, and a homogeneous grouping strategy based on reading annotations and previous group work experience can predict desirable group performance for this learning context. In addition, it also proposes the potential of automatic group formation with recommended parameter settings that leverage the results of predictive indicators.

The first part of the report presents stylized facts of youth and labor markets in Africa. The second part discusses past youth employment interventions in the region. It argues for the need of an integrated approach should governments want to tackle youth employment issues in a sustainable manner. Indeed, in African countries, with large informal sectors and dominance of rural population, solely reforming labor market institutions and implementing active labor market policies are likely to have limited impact. It argues that the most needed and well-rounded approaches are: expanding job and education alternatives in the rural areas, where most youth live; promoting and encouraging mobility; creating a conducive business environment; encouraging the private sector; improving the access and quality of skills formation; taking care of demographic issues that more directly affects the youth; and reducing child labor.

Comparing precarious employment (PE) across countries is essential to deepen the understanding of the phenomenon and to learn from country-specific experiences. However, this is hampered by the lack of internationally meaningful measures of PE. We aim to address this point by assessing the measurement invariance (MI) of the Employment Precariousness Scale for Europe (EPRES-E), an adaptation of the EPRES construct in the European Working Conditions Survey (EWCS). EPRES-E consists of 13 proxy-indicators sorted into six dimensions: temporariness, disempowerment, vulnerability, wages, exercise of rights, unpredictable working times. Drawing on EWCS-2015, MI of the second-order factor model was tested in a sample of 31,340 formal employees by means of (a) multi-group confirmatory factor analyses, and (b) the substantive exploration of EPRES-E mean scores in each country. The results demonstrate that threshold invariance holds for the first-order structure (dimensions) of 22 countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Lithuania, Luxembourg, the Netherlands, Norway, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, Switzerland, UK), but only metric invariance is attained by the second-order structure. The latter is supported by the exploration of mean scores, where we found that different score patterns in each dimension lead to similar overall EPRES-E scores, suggesting that PE is configured by different sources within the six dimensions in each country according to their broader socio-political trajectories. We conclude that, although EPRES-E can be used for comparative purposes in 22 European countries, the scores of each dimension must be reported alongside the overall EPRES-E score.

Among school districts in the greater Boston area, the lifting of masking requirements was associated with an additional 44.9 Covid-19 cases per 1000 students and staff during the 15 weeks after a statewide masking policy was rescinded.

Marine managers commonly use ecological indicators in planning and evaluations; however, few programs monitor social and cultural impacts of management. Practical approaches to identifying and monitoring social and cultural aspects of communities’ relationships with their environment could assist many agencies in understanding the impacts of their efforts to achieve conservation goals. The Hawaiʻi Department of Land and Natural Resources, Division of Aquatic Resources (DAR) launched the Holomua Marine Initiative to collaborate and engage with communities to strengthen co-management efforts, which included integrating socio-cultural aspects into the planning and assessment of marine management. Our team, which included resource managers, Western and indigenous scientists, community leaders, students, agency, and university staff engaged in collaborative management efforts in Hawaiʻi, developed an approach to monitor the social and cultural impacts of DAR’s management actions. Through online collaborative workshops with community members and non-profit leaders engaged in marine conservation in Hawaiʻi, we co-developed socio-cultural principles and indicators based on their reciprocal relationships with the nearshore environment. During the workshops, we used small group activities, snow cards, sorting, and categorization to generate nine fundamental principles, with associated indicators, to guide marine management in Hawaiʻi. Many of the principles and indicators are comparable to those developed in other parts of the Pacific, revolving around themes including the perpetuation of local and indigenous knowledge across generations, and access to land and natural resources. Participants also suggested themes less prevalent in other research, such as the need to evaluate impacts of tourism on community relationships with coastal areas. We offer recommendations for the development of socio-cultural principles and indicators in other place-based contexts, and emphasize the importance of on-going community collaboration. Developing a socio-cultural monitoring framework with community members impacted by marine management decisions could enable others engaged in collaborative efforts, including government agencies, to holistically understand and address impacts of their policies and actions. Monitoring layered socio-cultural impacts of marine management on local and indigenous communities has the potential to shift management goals, and enhance long-term effectiveness and support for initiatives to protect coastal resources worldwide.

Background Identify and compare health risk indicators for common chronic diseases between different occupational groups. Methods A total of 72,855 participants (41% women) participating in an occupational health service screening in 2014–2019 were included. Occupation was defined by the Swedish Standard Classification of Occupation, and divided into nine major and additionally eight sub-major groups. These were analysed separately, as white- and blue-collar occupations and as low- and high-skilled occupations. Seven health risk indicators were self-reported: exercise, physical work situation, sitting at work and leisure, smoking, diet, and perceived health, whereas cardiorespiratory fitness, BMI and blood pressure were measured. These were further dichotomized (yes/no) and as clustering of risk indicators (≥3 vs. <3). Results The greatest variation in OR across sub-major and major occupational groups were seen for daily smoking (OR = 0.68 to OR = 5.12), physically demanding work (OR = 0.55 to OR = 45.74) and high sitting at work (OR = 0.04 to OR = 1.86). For clustering of health risk indicators, blue-collar workers had significantly higher clustering of health risks (OR: 1.80; 95% CI 1.71–1.90) compared to white-collar workers (reference). Compared to high-skilled white-collar workers, low-skilled white-collar workers had similar OR (2.00; 1.88–2.13) as high-skilled blue-collar workers (1.98; 1.86–2.12), with low-skilled blue-collar workers having the highest clustered risk (2.32; 2.17–2.48). Conclusion There were large differences in health risk indicators across occupational groups, mainly between high-skilled white-collar occupations and the other occupations, with important variations also between major and sub-major occupational groups. Future health interventions should target the occupational groups identified with the highest risk for effective disease prevention.

Most poor children achieve less, exhibit more problem behaviors and are less healthy than children reared in more affluent families. We look beyond correlations such as these to a recent set of studies that attempt to assess the causal impact of childhood poverty on adult well-being. We pay particular attention to the potentially harmful effects of poverty early in childhood on adult labor market success (as measured by earnings), but also show results for other outcomes, including out-of-wedlock childbearing, criminal arrests and health status. Evidence suggests that early poverty has substantial detrimental effects on adult earnings and work hours, but on neither general adult health nor such behavioral outcomes as out-of-wedlock childbearing and arrests. We discuss implications for indicators tracking child well-being as well as policies designed to promote the well-being of children.

We conducted a field study of 71 action teams to examine the relationship between team mental model similarity and accuracy and the performance of real-world teams. We used Pathfinder to operationalize team members' taskwork mental models (describing team procedures, tasks, and equipment) and teamwork mental models (describing team interaction processes) and examined team performance as evaluated by expert team assessment center raters. Both taskwork mental model and teamwork mental model similarity predicted team performance. Team mental model accuracy measures were also predictive of team performance. We discuss the implications of our findings and directions for future research.

Hispanics or Latinos belong to the largest minoritized racial and ethnic group in the United States and represent a diverse group of people originating from at least 19 countries in Latin America and the Caribbean. Hereafter, Latino and Hispanic will be used interchangeably. Although their health status has been seen through the lens of the Hispanic paradox, or the idea of having similar or better health outcomes than the White majority despite their low socioeconomic indicators and access to care, there is still much to learn about the Hispanic population. Since the \"Hispanic paradox\" was coined in 1986, the Hispanic/Latino population has grown by 77%, from 14.5 to 63.7 million, coinciding with an increase in diversity, not only in sociodemographic characteristics but also in health status. The latter requires an increase in the number of health care and public health professionals to represent and understand the needs of this population.Latino scientists and activists formed the Chicano-Latino Caucus during the 1973 Annual Meeting of the American Public Health Association (APHA) in San Francisco, California. The Latino Caucus, as it is known today, had such an immediate influence on the Association that the 1974 meeting theme was declared \"The Health of the non-White and Poor Americans.\" Fifty years later, the Latino Caucus remains a pillar of public health and policy advocacy for Hispanic people. However, the same struggle from 1974 persists today: health inequities among the non-White minoritized population in America, and thus, the need to understand the health status of the Latino population, the largest US minoritized group.In this issue, we compiled a collection of empirical papers (n 5 4), analytical essays (n 5 4), notes from the field (n 5 3), and opinion editorials (n 5 8) discussing issues related to the health and well-being of the Hispanic population. These articles could be aggregated around issues of history and equity, determinants of health and disease prevention, immigration, discrimination, and the workforce, as well as diversity, identities, and data inequities.