Body composition assessment of English Premier League soccer players: a comparative DXA analysis of first team,U21 and U18 squads

Professional soccer players from the first team (1st team, n = 27), under twenty-one (U21, n = 21) and under eighteen (U18, n = 35) squads of an English Premier League soccer team were assessed for whole body and regional estimates of body composition using dual-energy X-ray absorptiometry (DXA). Per cent body fat was lower in 1st team (10.0 ± 1.6) compared with both U21 (11.6 ± 2.5, P = 0.02) and U18 (11.4 ± 2.6, P = 0.01) players. However, this difference was not due to variations (P = 0.23) in fat mass between squads (7.8 ± 1.6 v 8.8 ± 2.1 v 8.2 ± 2.4 kg, respectively) but rather the presence of more lean mass in 1st team (66.9 ± 7.1 kg, P < 0.01) and U21 (64.6 ± 6.5 kg, P = 0.02) compared with U18 (60.6 ± 6.3 kg) players. Accordingly, fat mass index was not different (P = 0.138) between squads, whereas lean mass index was greater (P < 0.01) in 1st team players (20.0 ± 1.1 kg · m^?2) compared with U18 players (18.8 ± 1.4 kg · m^?2). Differences in lean mass were also reflective of higher lean tissue mass in all regions, for example, upper limbs/lower limbs and trunk. Data suggest that training and nutritional interventions for younger players should therefore be targeted to lean mass growth as opposed to body fat loss.     

Increasing Student Engagement,Self-Efficacy,and Meta-Cognitive Self-Regulation in the High School Geometry Classroom: Do iPads Help?

Teachers are increasingly integrating mobile digital technology into the classroom. The purpose of this study was to assess the effect of incorporating iPads in a secondary-level geometry course on academic achievement, student engagement, self-efficacy, and meta-cognitive self-regulation. Students in the iPad-using classroom experienced lower levels of geometry proficiency scores, higher levels of off-task behaviors, and similar levels of self-efficacy and meta-cognitive self-regulation compared to the non-iPad group. However, the results may have been affected by several latent variables that can be controlled for in future research.     

Appraising research policy instrument mixes: a multicriteria mapping study in six European countries of diagnostic innovation to manage antimicrobial resistance

This article provides prospective appraisal of key policy instruments intended to stimulate innovation to combat antimicrobial resistance (AMR). AMR refers to the ability of microbes to evolve resistance to those treatments designed to kill them, and is associated with the overuse or misuse of medicines such as antibiotics. AMR is an emerging global challenge with major implications for healthcare and society as a whole. Diagnostic tests for infectious diseases can guide decision making when prescribing medicines, so reducing inappropriate drug use. In the context of growing international interest in policies to stimulate innovation in AMR diagnostics, this study uses multicriteria mapping (MCM) to appraise a range of policy instruments in order to understand their potential performance while also highlighting the uncertainties that stakeholders hold about such interventions in complex contexts. A contribution of the article is the demonstration of a novel method to analyse and visualise MCM data in order to reveal stakeholder inclinations towards particular options while exploring interviewees’ uncertainties about the effectiveness of each instrument's design or implementation. The article reports results from six European countries (Germany, Greece, Italy, the Netherlands, Spain and the UK). The findings reveal which policy instruments are deemed most likely to perform well, and why, across stakeholder groups and national settings, with areas of common ground and difference being identified. Importantly, the conclusions presented here differ from prominent policy discourse, with international implications for the design of mixes of policy instruments to combat AMR. Strategic and practical methodological implications also emerge for general appraisal of innovation policy instrument mixes.     

Interparental Violence and Childhood Adjustment: How and Why Maternal Sensitivity Is a Protective Factor

This study examined sensitive parenting as a protective factor in relations between interparental violence (IPV) and children's coping and psychological adjustment. Using a multimethod approach, a high‐risk sample of 201 two‐year‐olds and their mothers participated in three annual waves of data collection. Moderator analyses revealed that sensitive parenting buffered the risk posed by IPV on children's changes in externalizing and prosocial development over a 2‐year period. Tests of mediated moderation further indicated that sensitive parenting protected children from the vulnerability of growing up in a violent home through its association with lower levels of children's angry reactivity to interparental conflict. Results highlight the significance of identifying the mechanisms that mediate protective factors in models of family adversity.     

NAVIGATING SOCIAL NETWORKING AND SOCIAL MEDIA IN SCHOOL PSYCHOLOGY: ETHICAL AND PROFESSIONAL CONSIDERATIONS IN TRAINING PROGRAMS

Social networking and social media have undoubtedly proliferated within the past decade, allowing widespread communication and dissemination of user‐generated content and information. Some psychology graduate programs, including school psychology, have started to embrace social networking and media for instructional and training purposes; however, there have not been any consistent ethical guidelines or standards among training programs relating to how trainers, graduate students, and practitioners should use social networking and social media professionally. This article draws on the current yet limited research on social networking and social media, and addresses the ethical challenges when managing both professional and personal online identities in school psychology. Two relevant case scenarios are discussed, along with suggested guidelines for developing technological policies for school psychologists. Future directions in research and practical implications for trainers, graduate students, and practitioners are explored.     

Collingridge and the dilemma of control: Towards responsible and accountable innovation

The paper critically reviews the work of David Collingridge in the light of contemporary concerns about responsibility and accountability in innovation, public engagement with science and technology, and the role of scientific expertise in technology policy. Given continued interest in his thoughts on the ‘social control of technology’, and the ‘dilemma of control’, this attention is both timely and overdue. The paper illuminates a mismatch between the prevalence of citations to Collingridge’s work on the dilemma of control in the literature on responsible innovation, and the depth of engagement with his arguments. By considering neglected aspects of Collingridge’s substantive, methodological and philosophical analysis, important implications can be drawn for theory and practice relating to the governance of innovation and co-evolution between technology and society. The paper helps to improve understandings of wider political contexts for responsible innovation, especially in relation to anticipatory, participatory and institutional aspects of governance.     

When neighboring disciplines fail to learn from each other: The case of innovation and project management research

As knowledge production becomes more specialized, studying complex and multi-faceted empirical realities becomes more difficult. This has created a growing need for cross-fertilization and collaboration between research disciplines. According to prior studies, the sharing of concepts, ideas and empirical domains with other disciplines may promote cross-fertilization. We challenge this one-sided view. Based on an analysis of the parallel development of the neighboring disciplines of innovation studies and project management, we show that the sharing of concepts and empirical domains can have ambivalent effects. Under conditions of ideological distancing, shared concepts and domains will be narrowly assimilated ? an effect we call ‘encapsulation’ – which creates an illusion of sharing, while promoting further self-containment. By comparison, reflexive meta-theories and cross-disciplinary community-building will enable a form of sharing that promotes cross-fertilization. Our findings inform research on research specialization, cross-fertilization and effectiveness of interdisciplinary collaboration.     

Human and artificial intelligence collaboration for socially shared regulation in learning

Artificial intelligence (AI) has generated a plethora of new opportunities, potential and challenges for understanding and supporting learning. In this paper, we position human and AI collaboration for socially shared regulation (SSRL) in learning. Particularly, this paper reflects on the intersection of human and AI collaboration in SSRL research, which presents an exciting prospect for advancing our understanding and support of learning regulation. Our aim is to operationalize this human-AI collaboration by introducing a novel trigger concept and a hybrid human-AI shared regulation in learning (HASRL) model. Through empirical examples that present AI affordances for SSRL research, we demonstrate how humans and AI can synergistically work together to improve learning regulation. We argue that the integration of human and AI strengths via hybrid intelligence is critical to unlocking a new era in learning sciences research. Our proposed frameworks present an opportunity for empirical evidence and innovative designs that articulate the potential for human-AI collaboration in facilitating effective SSRL in teaching and learning.

Practitioner notes

What is already known about this topic

• For collaborative learning to succeed, socially shared regulation has been acknowledged as a key factor.
• Artificial intelligence (AI) is a powerful and potentially disruptive technology that can reveal new insights to support learning.
• It is questionable whether traditional theories of how people learn are useful in the age of AI.

What this paper adds

• Introduces a trigger concept and a hybrid Human-AI Shared Regulation in Learning (HASRL) model to offer insights into how the human-AI collaboration could occur to operationalize SSRL research.
• Demonstrates the potential use of AI to advance research and practice on socially shared regulation of learning.
• Provides clear suggestions for future human-AI collaboration in learning and teaching aiming at enhancing human learning and regulatory skills.

Implications for practice and/or policy

• Educational technology developers could utilize our proposed framework to better align technological and theoretical aspects for their design of adaptive support that can facilitate students' socially shared regulation of learning.
• Researchers and practitioners could benefit from methodological development incorporating human-AI collaboration for capturing, processing and analysing multimodal data to examine and support learning regulation.