Clase social y movilidad social en España e Italia

Utilizando las encuestas nacionales que durante los primeros años de la década de 1960 analizaron los varones españoles e italianos, se estudian aquí las tasas de movilidad a lo largo de tres generaciones dentro de los países, y, entre los países, en dos momentos en el tiempo. Los resultados obtenidos con la aplicación de una serie de medidas concretas de las tasas de movilidad, como así también las medidas de interacción de Goodman, y la herencia del estatus intrínseca referida a diferentes aspectos o partes de las matrices, se revelan sustanciales dentro del crecimiento del país en las tasas correspondientes tanto a la movilidad observada como en circulación, como así también cambios significativos operados en las interacciones relacionadas con dos pares de estatus: no manual-manual, y manual-agrícola. Solamente en un caso, el estatus manual en España, se observa una declinación significativa en la firmeza de la herencia del estatus intrínseca. Las comparaciones entre países confirman las hipótesis de Hauser-Featherman sobre la similitud que manifiesta la movilidad de circulación a través de las sociedades industriales. Las tasas de la movilidad observada entre los países se manifiestan distintas en la comparación de los estatus de encuestado-padre. Llegamos a la conclusión de que ha tenido lugar un proceso de convergencia en los procesos de movilidad de España e Italia ocurridos durante el tiempo representado por las dos tablas, y tomamos la precaución necesaria contra la tendencia a realizar generalizaciones con demasiada ligereza en relación con otros países industriales

Family Life Course Statuses and Transitions: Relationships with Health Limitations

In this study, the author uses 25 years of data taken from the 1979 National Longitudinal Study of Youth to examine the relationship between family life course statuses and transitions and work-related health limitations. The author uses a detailed set of statuses and transitions that include marriage, divorce, cohabitation, and parenthood. The measures of health used tap health limitations in the kind and amount of work that can be performed. Using a fixed-effects estimator for dichotomous outcomes, the author finds that marriage is positively related to the health of men but negatively related to the health of women. The author also finds that parenthood is not related to the health of men but is positively related to the health of women. The results also indicate that statuses are more important for determining health limitations than are transitions

How reliable are knee kinematics and kinetics during side-cutting manoeuvres?

INTRODUCTION: Side-cutting tasks are commonly used in dynamic assessment of ACL injury risk, but only limited information is available concerning the reliability of knee loading parameters. The aim of this study was to investigate the reliability of side-cutting data with additional focus on modelling approaches and task execution variables. METHODS: Each subject (n=8) attended six testing sessions conducted by two observers. Kinematic and kinetic data of 45° side-cutting tasks was collected. Inter-trial, inter-session, inter-observer variability and observer/trial ratios were calculated at every time-point of normalised stance, for data derived from two modelling approaches. Variation in task execution variables was regressed against that of temporal profiles of relevant knee data using one-dimensional statistical parametric mapping. RESULTS: Variability in knee kinematics was consistently low across the time-series waveform (≤5°), but knee kinetic variability was high (31.8, 24.1 and 16.9Nm for sagittal, frontal and transverse planes, respectively) in the weight acceptance phase of the side-cutting task. Calculations conveyed consistently moderate-to-good measurement reliability. Inverse kinematic modelling reduced the variability in sagittal (∼6Nm) and frontal planes (∼10Nm) compared to direct kinematic modelling. Variation in task execution variables did not explain any knee data variability. CONCLUSION: Side-cutting data appears to be reliably measured, however high knee moment variability exhibited in all planes, particularly in the early stance phase, suggests cautious interpretation towards ACL injury mechanics. Such variability may be inherent to the dynamic nature of the side-cutting task or experimental issues not yet known

The role of parental achievement goals in predicting autonomy-supportive and controlling parenting

Although autonomy-supportive and controlling parenting are linked to numerous positive and negative child outcomes respectively, fewer studies have focused on their determinants. Drawing on achievement goal theory and self-determination theory, we propose that parental achievement goals (i.e., achievement goals that parents have for their children) can be mastery, performance-approach or performance-avoidance oriented and that types of goals predict mothers' tendency to adopt autonomy-supportive and controlling behaviors. A total of 67 mothers (aged 30-53 years) reported their goals for their adolescent (aged 13-16 years; 19.4 % girls), while their adolescent evaluated their mothers' behaviors. Hierarchical regression analyses showed that parental performance-approach goals predict more controlling parenting and prevent acknowledgement of feelings, one autonomy-supportive behavior. In addition, mothers who have mastery goals and who endorse performance-avoidance goals are less likely to use guilt-inducing criticisms. These findings were observed while controlling for the effect of maternal anxiety

Do helpful mothers help? Effects of maternal scaffolding and infant motivation on cognitive performance

Infants are highly social and much early learning takes place in a social context during interactions with caregivers. Previous research shows that social scaffolding – responsive parenting and joint attention - can confer benefits for infants’ long-term development and learning. However, little previous research has examined whether dynamic (moment-to-moment) adaptations in adults’ social scaffolding are able to produce immediate effects on infants' performance. Here we ask whether infants' success on an object search task is more strongly influenced by maternal behaviour, including dynamic changes in response behaviour, or by fluctuations in infants' own engagement levels. Thirty-five mother-infant dyads (infants aged 10.8 months, on average) participated in an object search task that was delivered in a naturalistic manner by the child’s mother. Measures of maternal responsiveness (teaching duration; sensitivity) and infant engagement (engagement score; visual attention) were assessed. Mothers varied their task delivery trial by trial, but neither measure of maternal responsiveness significantly predicted infants’ success in performing the search task. Rather, infants’ own level of engagement was the sole significant predictor of accuracy. These results indicate that while parental scaffolding is offered spontaneously (and is undoubtedly crucial for development), in this context children’s endogenous engagement proved to be a more powerful determinant of task success. Future work should explore this interplay between parental and child-internal factors in other learning and social contexts

What Stimulates Researchers to Make Their Research Usable? Towards an Openness Approach

Ambiguity surrounding the effect of external engagement on academic research has raised questions about what motivates researchers to collaborate with third parties. We argue that what matters for society is research that can be absorbed by users. We define openness as a willingness by researchers to make research more usable by external partners by responding to external influences in their own research practices. We ask what kinds of characteristics define those researchers who are more open to creating usable knowledge. Our empirical study analyses a sample of 1583 researchers working at the Spanish Council for Scientific Research (CSIC). Results demonstrate that it is personal factors (academic identity and past experience) that determine which researchers have open behaviours. The paper concludes that policies to encourage external engagement should focus on experiences which legitimate and validate knowledge produced through user encounters, both at the academic formation career stage as well as through providing ongoing opportunities to engage with third parties.The data used for this study comes from the IMPACTO project funded by the Spanish Council for Scientific Research - CSIC (Ref. 200410E639). The work also benefited from a mobility grant awarded by Eu-Spri Forum to Julia Olmos Penuela & Paul Benneworth for her visiting research to the Center of Higher Education Policy Studies. Finally, Julia Olmos Penuela also benefited from a post-doctoral grant funded by the Generalitat Valenciana (APOSTD-2014-A-006).Olmos-Peñuela, J.; Benneworth, P.; Castro-Martínez, E. (2015). What Stimulates Researchers to Make Their Research Usable? Towards an Openness Approach. Minerva. 53(4):381-410. https://doi.org/10.1007/s11024-015-9283-4S381410534Abreu, Maria, Vadim Grinevich, Alan Hughes, and Michael Kitson. 2009. Knowledge exchange between academics and the business, public and third sectors. Cambridge: Centre for Business Research and UK-IRC.Aghion, Philippe, Mathias Dewatripont, and Jeremy C. Stein. 2008. Academic freedom, private-sector focus, and the process of innovation. RAND Journal of Economics 39: 617–635.Ajzen, Icek. 2001. Nature and operation of attitudes. Annual Review of Psychology 52(1): 27–58.Alrøe, Hugo Fjelsted, and Erik Steen Kristensen. 2002. Towards a systemic research methodology in agriculture: Rethinking the role of values in science. Agriculture and Human Values 19(1): 3–23.Audretsch, David B., Werner Bönte, and Stefan Krabel. 2010. Why do scientists in public research institutions cooperate with private firms. In DRUID Working Paper, 10–27.Baldini, Nicola, Rosa Grimaldi, and Maurizio Sobrero. 2007. To patent or not to patent? A survey of Italian inventors on motivations, incentives, and obstacles to university patenting. Scientometrics 70(2): 333–354.Bandura, Albert. 1977. Social learning theory. Englewood Cliffs, NJ: Prentice-Hall.Barnett, R. 2009. Knowing and becoming in the higher education curriculum. Studies in Higher Education 34(4): 429–440.Becher, Tony. 1994. The significance of disciplinary differences. Studies in Higher Education 19(2): 151–161.Becher, Tony, and Paul Trowler. 2001. Academic tribes and territories: Intellectual enquiry and the culture of disciplines. McGraw-Hill International.Bekkers, Rudi, and Isabel Maria Bodas Freitas. 2008. Analysing knowledge transfer channels between universities and industry: To what degree do sectors also matter? Research Policy 37(10): 1837–1853.Belderbos, René, Martin Carree, Bert Diederen, Boris Lokshin, and Reinhilde Veugelers. 2004. Heterogeneity in R&D cooperation strategies. International Journal of Industrial Organization 22(8): 1237–1263.Benner, Mats, and Ulf Sandström. 2000. Institutionalizing the triple helix: Research funding and norms in the academic system. Research Policy 29(2): 291–301.Bercovitz, Janet, and Maryann Feldman. 2008. Academic entrepreneurs: Organizational change at the individual level. Organization Science 19(1): 69–89.Berman, Elizabeth Popp. 2011. Creating the market university: How academic science became an economic engine. Princeton University Press.Bleiklie, Ivar, and Roar Høstaker. 2004. Modernizing research training-education and science policy between profession, discipline and academic institution. Higher Education Policy 17(2): 221–236.Bozeman, Barry, Daniel Fay, and Catherine P. Slade. 2013. Research collaboration in universities and academic entrepreneurship: The-state-of-the-art. The Journal of Technology Transfer 38(1): 1–67.Collini, Stefan. 2009. Impact on humanities: Researchers must take a stand now or be judged and rewarded as salesmen. The Times Literary Supplement 5563: 18–19.D’Este, Pablo, and Markus Perkmann. 2011. Why do academics engage with industry? The entrepreneurial university and individual motivations. The Journal of Technology Transfer 36(3): 316–339.D’Este, Pablo, Oscar Llopis, and Alfredo Yegros. 2013. Conducting pro-social research: Cognitive diversity, research excellence and awareness about the social impact of research: INGENIO (CSIC-UPV) Working Paper Series.Deem, Rosemary, and Lisa Lucas. 2007. Research and teaching cultures in two contrasting UK policy contexts: Academic life in education departments in five English and Scottish universities. Higher Education 54(1): 115–133.DiMaggio, Paul J., and Walter W. Powell. 1983. The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review 48(2): 147–160.Downing, David B. 2005. The knowledge contract: Politics and paradigms in the academic workplace. Lincoln: Nebraska University of Nebraska Press.Donovan, Claire. 2007. The qualitative future of research evaluation. Science and Public Policy 34(8): 585–597.Durning, Bridget. 2004. Planning academics and planning practitioners: Two tribes or a community of practice? Planning Practice and Research 19(4): 435–446.Edquist, Charles. 1997. System of innovation approaches: Their emergence and characteristics. In Systems of innovation: Technologies, institutions and organizations, ed. C. Edquist, 1–35. London: Pinter.Etzkowitz, Henry, and Loet Leydesdorff. 2000. The dynamics of innovation: from National Systems and “Mode 2” to a Triple Helix of university–industry–government relations. Research Policy 29(2): 109–123.Fromhold-Eisebith, Martina, Claudia Werker, and Marcel Vojnic. 2014. Tracing the social dimension in innovation networks. In The social dynamics of innovation networks, eds. Roel Rutten, Paul Benneworth, Frans Boekema, and Dessy Irawati. London: Routledge (in press).Geuna, Aldo, and Alessandro Muscio. 2009. The governance of university knowledge transfer: A critical review of the literature. Minerva 47(1): 93–114.Gibbons, Michael, Camille Limoges, Helga Nowotny, Simon Schwartzman, Peter Scott, and Martin Trow. 1994. The new production of knowledge: The dynamics of science and research in contemporary societies. London: Sage.Gläser, Jochen. 2012. How does Governance change research content? On the possibility of a sociological middle-range theory linking science policy studies to the sociology of scientific knowledge. Technical University Berlin. Technology Studies Working Papers. http://www.ts.tu-berlin.de/fileadmin/fg226/TUTS/TUTS-WP-1-2012.pdf . Accessed 16 Feb 2015.Goethner, Maximilian, Martin Obschonka, Rainer K. Silbereisen, and Uwe Cantner. 2012. Scientists’ transition to academic entrepreneurship: Economic and psychological determinants. Journal of Economic Psychology 33(3): 628–641.Gulbrandsen, Magnus, and Jens-Christian Smeby. 2005. Industry funding and university professors’ research performance. Research Policy 34(6): 932–950.Haeussler, Carolin, and Jeannette Colyvas. 2011. Breaking the ivory tower: Academic entrepreneurship in the life sciences in UK and Germany. Research Policy 40(1): 41–54.Hessels, Laurens K., Harro van Lente, John Grin, and Ruud E.H.M. Smits. 2011. Changing struggles for relevance in eight fields of natural science. Industry and Higher Education 25(5): 347–357.Hessels, Laurens K., and Harro Van Lente. 2008. Re-thinking new knowledge production: A literature review and a research agenda. Research Policy 37(4): 740–760.Hoye, Kate, and Fred Pries. 2009. ‘Repeat commercializers’, the ‘habitual entrepreneurs’ of university–industry technology transfer. Technovation 29(10): 682–689.Jacobson, Nora, Dale Butterill, and Paula Goering. 2004. Organizational factors that influence university-based researchers’ engagement in knowledge transfer activities. Science Communication 25(3): 246–259.Jain, Sanjay, Gerard George, and Mark Maltarich. 2009. Academics or entrepreneurs? Investigating role identity modification of university scientists involved in commercialization activity. Research Policy 38(6): 922–935.Jasanoff, Sheila, and Sang-Hyun Kim. 2013. Sociotechnical imaginaries and national energy policies. Science as Culture 22(2): 189–196.Jensen, Pablo. 2011. A statistical picture of popularization activities and their evolutions in France. Public Understanding of Science 20(1): 26–36.Kitcher, Philip. 2001. Science, truth, and democracy. Oxford: Oxford University Press.Knorr-Cetina, Karin. 1981. The manufacture of knowledge: An essay on the constructivist and contextual nature of science. Oxford: Pergamon Press.Kronenberg, Kristin, and Marjolein Caniëls. 2014. Professional proximity in research collaborations. In The social dynamics of innovation networks, eds. Roel Rutten, Paul Benneworth, Frans Boekema, and Dessy Irawati. London: Routledge (in press).Krueger, Rob, and David Gibbs. 2010. Competitive global city regions and sustainable development’: An interpretive institutionalist account in the South East of England. Environment and planning A 42: 821–837.Lam, Alice. 2011. What motivates academic scientists to engage in research commercialization: ‘Gold’, ‘ribbon’ or ‘puzzle’? Research Policy 40(10): 1354–1368.Landry, Réjean, Malek Saïhi, Nabil Amara, and Mathieu Ouimet. 2010. Evidence on how academics manage their portfolio of knowledge transfer activities. Research Policy 39(10): 1387–1403.Lee, Alison, and David Boud. 2003. Writing groups, change and academic identity: Research development as local practice. Studies in Higher Education 28(2): 187–200.Lee, Yong S. 1996. ‘Technology transfer’ and the research university: A search for the boundaries of university–industry collaboration. Research Policy 25(6): 843–863.Lee, Yong S. 2000. The sustainability of university–industry research collaboration: An empirical assessment. The Journal of Technology Transfer 25(2): 111–133.Leisyte, Liudvika, Jürgen Enders, and Harry De Boer. 2008. The freedom to set research agendas—illusion and reality of the research units in the Dutch Universities. Higher Education Policy 21(3): 377–391.Louis, Karen Seashore, David Blumenthal, Michael E. Gluck, and Michael A. Stoto. 1989. Entrepreneurs in academe: An exploration of behaviors among life scientists. Administrative Science Quarterly 34(1): 110–131.Lowe, Philip, Jeremy Phillipson, and Katy Wilkinson. 2013. Why social scientists should engage with natural scientists. Contemporary Social Science 8(3): 207–222.Martín-Sempere, María José, Belén Garzón-García, and Jesús Rey-Rocha. 2008. Scientists’ motivation to communicate science and technology to the public: Surveying participants at the Madrid Science Fair. Public Understanding of Science 17(3): 349–367.Martin, Ben. 2003. The changing social contract for science and the evolution of the university. In Science and innovation: Rethinking the rationales for funding and governance, eds. A. Geuna, A.J. Salter, and W.E. Steinmueller, 7–29. Cheltenhan: Edward Elgar.Merton, Robert K. 1973. The sociology of science: Theoretical and empirical investigations. Chicago: University of Chicago Press.Miller, Thaddeus R., and Mark W. Neff. 2013. De-facto science policy in the making: how scientists shape science policy and why it matters (or, why STS and STP scholars should socialize). Minerva 51(3): 295–315.Muthén, Bengt O. 1998–2004. Mplus Technical Appendices. Muthén & Muthén. Los Angeles, CA.: Muthén & Muthén.Nedeva, Maria. 2013. Between the global and the national: Organising European science. Research Policy 42(1): 220–230.Neff, Mark William. 2014. Research prioritization and the potential pitfall of path dependencies in coral reef science. Minerva 52(2): 213–235.Nelson, Richard R. 2001. Observations on the post-Bayh-Dole rise of patenting at American universities. The Journal of Technology Transfer 26(1): 13–19.Nowotny, Helga, Peter Scott, and Michael Gibbons. 2001. Re-thinking science: Knowledge and the public in an age of uncertainty. Cambridge: Polity Press.Olmos-Peñuela, Julia, Paul Benneworth, and Elena Castro-Martínez. 2014a. Are ‘STEM from Mars and SSH from Venus’? Challenging disciplinary stereotypes of research’s social value. Science and Public Policy 41: 384–400.Olmos-Peñuela, Julia, Elena Castro-Martínez, and Manuel Fernández-Esquinas. 2014b. Diferencias entre áreas científicas en las prácticas de divulgación de la investigación: un estudio empírico en el CSIC. Revista Española de Documentación Científica. doi: 10.3989/redc.2014.2.1096 .Ouimet, Mathieu, Nabil Amara, Réjean Landry, and John Lavis. 2007. Direct interactions medical school faculty members have with professionals and managers working in public and private sector organizations: A cross-sectional study. Scientometrics 72(2): 307–323.Perkmann, Markus, Valentina Tartari, Maureen McKelvey, Erkko Autio, Anders Brostrom, Pablo D’Este, Riccardo Fini, et al. 2013. Academic engagement and commercialisation: A review of the literature on university-industry relations. Research Policy 42(2): 423–442.Philpott, Kevin, Lawrence Dooley, Caroline O’Reilly, and Gary Lupton. 2011. The entrepreneurial university: Examining the underlying academic tensions. Technovation 31(4): 161–170.Rutten, Roel, and Frans Boekema. 2012. From learning region to learning in a socio-spatial context. Regional Studies 46(8): 981–992.Sarewitz, Daniel, and Roger A. Pielke. 2007. The neglected heart of science policy: reconciling supply of and demand for science. Environmental Science & Policy 10(1): 5–16.Sauermann, Henry, and Paula Stephan. 2013. Conflicting logics? A multidimensional view of industrial and academic science. Organization Science 24(3): 889–909.Schein, Edgar H. 1985. Organizational culture and leadership: A dynamic view. San Francisco, CA: Jossey-Bass.Shane, Scott. 2000. Prior knowledge and the discovery of entrepreneurial opportunities. Organization Science 11(4): 448–469.Spaapen, Jack, and Leonie van Drooge. 2011. Introducing ‘productive interactions’ in social impact assessment. Research Evaluation 20(3): 211–218.Stokes, Donald E. 1997. Pasteur’s quadrant: Basic science and technological innovation. Washington, DC: Brookings Institution Press.Tartari, Valentina, and Stefano Breschi. 2012. Set them free: scientists’ evaluations of the benefits and costs of university–industry research collaboration. Industrial and Corporate Change 21(5): 1117–1147.Tinker, Tony, and Rob Gray. 2003. Beyond a critique of pure reason: From policy to politics to praxis in environmental and social research. Accounting, Auditing & Accountability Journal 16(5): 727–761.van Rijnsoever, Frank J., Laurens K. Hessels, and Rens L.J. Vandeberg. 2008. A resource-based view on the interactions of university researchers. Research Policy 37(8): 1255–1266.Venkataraman, Sankaran. 1997. The distinctive domain of entrepreneurship research: An editor’s perspective. Advances in Entrepreneurship, Firm Emergence, and Growth 3: 119–138.Verspagen, Bart. 2006. University research, intellectual property rights and European innovation systems. Journal of Economic Surveys 20(4): 607–632.Villanueva-Felez, Africa, Jordi Molas-Gallart, and Alejandro Escribá-Esteve. 2013. Measuring personal networks and their relationship with scientific production. Minerva 51(4): 465–483.Watermeyer, Richard. 2015. Lost in the ‘third space’: the impact of public engagement in higher education on academic identity, research practice and career progression. European Journal of Higher Education (online first, doi: 10.1080/21568235.2015.1044546 ).Weingart, Peter. 2009. Editorial for Issue 47/3. Minerva 47(3): 237–239.Ziman, John. 1996. ‘Postacademic science’: Constructing knowledge with networks and norms. Science Studies 1: 67–80.Zomer, Arend H., Ben W.A. Jongbloed, and Jürgen Enders. 2010. Do spin-offs make the academics’ heads spin? The impacts of spin-off companies on their parent research organisation. Minerva 48(3): 331–353

Mapping current research trends on anterior cruciate ligament injury risk against the existing evidence: In vivo biomechanical risk factors.

BACKGROUND: Whilst many studies measure large numbers of biomechanical parameters and associate these to anterior cruciate ligament injury risk, they cannot be considered as anterior cruciate ligament injury risk factors without evidence from prospective studies. A review was conducted to systematically assess the in vivo biomechanical literature to identify biomechanical risk factors for non-contact anterior cruciate ligament injury during dynamic sports tasks; and to critically evaluate the research trends from retrospective and associative studies investigating non-contact anterior cruciate ligament injury risk. METHODS: An electronic literature search was undertaken on studies examining in vivo biomechanical risk factors associated with non-contact anterior cruciate ligament injury. The relevant studies were assessed by classification; level 1 - a prospective cohort study, level 2 - a retrospective study or level 3 - an associative study. FINDINGS: An initial search revealed 812 studies but this was reduced to 1 level 1 evidence study, 20 level 2 evidence studies and 175 level 3 evidence studies that met all inclusion criteria. Level 1 evidence showed that the knee abduction angle, knee abduction moment and ground reaction force were biomechanical risk factors. Nine level 2 studies and eighty-three level 3 studies used these to assess risk factors in their study. Inconsistencies in results and methods were observed in level 2 and 3 studies. INTERPRETATION: There is a lack of high quality, prospective level 1 evidence related to biomechanical risk factors for non-contact anterior cruciate ligament injury. More prospective cohort studies are required to determine risk factors and provide improved prognostic capability

A framework for evolutionary systems biology

<p>Abstract</p> <p>Background</p> <p>Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects.</p> <p>Results</p> <p>Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions <it>in silico</it>. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism.</p> <p>Conclusion</p> <p>EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications.</p

Mapping current research trends on neuromuscular risk factors of non-contact ACL injury.

The aim of this systematic review was (i) to identify neuromuscular markers that have been predictive of a primary non-contact ACL injury, (ii) to assess whether proposed risk factors have been supported or refuted in the literature from cohort and case-control studies, and (iii) to reflect on the body of research that aims at developing field based tools to assess risk through an association with these risk factors. Electronic searches were undertaken, of PubMed, SCOPUS, Web of Science, CINAHL and SPORTDiscus examining neuromuscular risk factors associated with ACL injury published between January 1990 and July 2015. The evidence supporting neuromuscular risk factors of ACL injury is limited where only 4 prospective cohort studies were found. Three of which looked into muscular capacity and one looked into muscular activation patterns but none of the studies found strong evidence of how muscular capacity or muscular activation deficits are a risk factor for a primary non-contact ACL injury. A number of factors associated to neural control and muscular capacity have been suggested to be related to non-contact ACL injury risk but the level of evidence supporting these risk factors remains often elusive, leaving researchers and practitioners uncertain when developing evidence-based injury prevention programs

A global experiment on motivating social distancing during the COVID-19 pandemic

Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This cross-country, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e., a controlling message) compared with no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared with the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly internalized form of motivation relying on one’s core values) or behavioral intentions. Results supported hypothesized associations between people’s existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing. Controlled motivation was associated with more defiance and less long-term behavioral intention to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges