Review of practice-led research in art, design & architecture

This review report sets out the outcomes of a 10 month investigation to describe the landscape of practice-led research in Art, Design and Architecture (ADA) in the UK and beyond. We were asked for a qualitative review but of course it has been important to gather some numbers to check and illustrate our observations. We have consulted widely, both face to face and in the virtual world, with experts and novices in the UK and around the world. We have tried to strike a balance between the natural desire of our colleagues to debate the more contentious aspects of this territory (they were never going to forgo that opportunity) and the equally strong wish of the AHRC that we should provide a clear description of what is happening. We have collected some diverse examples of research and subjected them to various examinations. We have also examined a selection of research projects funded by AHRC and other projects by creative practitioners, funded by a non-research organisation. From all this we have been able to describe the landscape in a straightforward sense: We have measures of the proportions of ADA academics involved in practice-led research. We have clarified differences in the ways that the different ADA disciplines engage with practice-led research and identified some problems that indicate possible future support strategies. We have discussed some problems with general definitions of research and identified issues that should be addressed to ensure that the AHRC definition can be applied to the full range of practice-led research. We have picked out some specific case examples that illustrate the range of contexts, methods and contributions made by practice-led researchers, and more are described in detail in Appendix F. We have also sought to assess how this research relates to the wider international picture in which the UK appears to have a strong position in both volume and development of research. We have also set out some issues that affect this community of researchers: What strengths and weaknesses have we observed and where is there a need to support development? Do the AHRC definition of research and guidance on practice-led research provide an effective framework? We have illustrated the state of development of research in ADA, and some reasons why it is less robust than might be expected from such long established disciplines. We recommend that the career path of researchers in ADA needs some attention and make some suggestions about how that could be achieved. We have also indicated some areas of inquiry that might be supported to advance the theory and methods of practice-led research. In particular we have come to the conclusion that conventional ideas of contribution to knowledge or understanding may not be serving us well. This is significant to fine artists but we believe that it relevant across ADA and a shared effort to develop appropriate new models would be a constructive development. The full set of recommendations can be found in chapter 5

Retrospective analysis of anthropometric and fitness characteristics associated with long-term career progression in Rugby League.

The current study retrospectively investigated the differences in anthropometric and fitness characteristics of junior rugby league players selected onto a talent identification and development (TID) programme between long-term career progression levels (i.e., amateur, academy, professional)

Variable and changing trajectories in youth athlete development: further verification in advocating a long-term inclusive tracking approach.

Athlete development through adolescence can vary greatly because of maturational processes. For example, variation can be observed in anthropometric and fitness measures with later maturing individuals "catching up" their earlier maturing peers at later time points. This study examined a methodological issue concerning how best to assess anthropometric and fitness change (i.e., "across age categories" or "per year") relative to an age and skill-matched population (N = 1,172). Furthermore, it examined changes in anthropometric and fitness characteristics in 3 cases of youth rugby league players (aged 13-15) across a 2-year period. Findings identified the "per year" method as generating less deviated z-scores across anthropometric and fitness measures (e.g., mean change p < 0.001), suggesting less substantial change in case players relative to the population. When applied to additional players, z-score and radar graphs showed developmental variability and longitudinal change. The possibility of a "later maturing player" increasing anthropometric (e.g., height: player 4 = 3.3 cm; player 5 = 13.2 cm; and player 6 = 15.7 cm) and fitness (e.g., 30-m sprint: player 4 = -0.18 s, player 5 = -0.46 s, and player 6 = -0.59 s) characteristics compared with early maturing players was confirmed. Findings affirm the potential for variable and changing trajectories in adolescent athletes. Practical implications advocate a long-term inclusive tracking approach of athletes, the avoidance of (de)selection, and the reduction of a performance emphasis in adolescent stages of sport systems

An individualized longitudinal approach to monitoring the dynamics of growth and fitness development in adolescent athletes.

This study evaluated the development of anthropometric and fitness characteristics of 3 individual adolescent junior rugby league players and compared their characteristics with a cross-sectional population matched by age and skill level. Cross-sectional anthropometric and fitness assessments were conducted on 1,172 players selected to the Rugby Football League's talent development program (i.e., the Player Performance Pathway) between 2005 and 2008. Three players of differing relative age, maturational status, and playing position were measured and tracked once per year on 3 occasions (Under 13s, 14s, 15s age categories) and compared against the cross-sectional population. Results demonstrated that the later maturing players increased height (player 1 = 9.2%; player 2 = 7.8%) and a number of fitness characteristics (e.g., 60-m speed-player 1 = -14.9%; player 2 = -9.9%) more than the earlier maturing player (player 3-Height = 2.0%, 60-m sprint = -0.7%) over the 2-year period. The variation in the development of anthropometric and fitness characteristics between the 3 players highlights the importance of longitudinally monitoring individual characteristics during adolescence to assess the dynamic changes in growth, maturation, and fitness. Findings showcase the limitations of short-term performance assessments at one-off time points within annual-age categories, instead of advocating individual development and progression tracking without deselection. Coaches should consider using an individual approach, comparing data with population averages, to assist in the prescription of appropriate training and lifestyle interventions to aid the development of junior athletes

Using anthropometric and performance characteristics to predict selection in junior UK Rugby League players.

Research examining the factors influencing selection within talented junior Rugby League players is limited. The aims of this study were firstly to determine whether differences existed for anthropometric and performance characteristics between regional and national selection in high performance UK junior Rugby League players, and secondly to identify variables that discriminated between these selection levels. Regional representative (n=1172) selected junior players (aged 13-16 years) undertook an anthropometric and fitness testing battery with players split according to selection level (i.e., national, regional). MANCOVA analyses, with age and maturation controlled, identified national players as having lower sum of 4 skinfolds scores compared to regional players, and also performed significantly better on all physical tests. Stepwise discriminant analysis identified that estimated maximum oxygen uptake (VO2max), chronological age, body mass, 20 m sprint, height, sum of 4 skinfolds and sitting height discriminated between selection levels, accounting for 28.7% of the variance. This discriminant analysis corresponded to an overall predictive accuracy of 63.3% for all players. These results indicate that performance characteristics differed between selection levels in junior Rugby League players. However, the small magnitude of difference between selection levels suggests that physical qualities only partially explain higher representative selection. The monitoring and evaluation of such variables, alongside game related performance characteristics, provides greater knowledge and understanding about the processes and consequences of selection, training and performance in youth sport

Capturing the spatial variability of HIV epidemics in South Africa and Tanzania using routine healthcare facility data

Background: Large geographical variations in the intensity of the HIV epidemic in sub-Saharan Africa call for geographically targeted resource allocation where burdens are greatest. However, data available for mapping the geographic variability of HIV prevalence and detecting HIV ‘hotspots’ is scarce, and population-based surveillance data are not always available. Here, we evaluated the viability of using clinic-based HIV prevalence data to measure the spatial variability of HIV in South Africa and Tanzania. Methods: Population-based and clinic-based HIV data from a small HIV hyper-endemic rural community in South Africa as well as for the country of Tanzania were used to map smoothed HIV prevalence using kernel interpolation techniques. Spatial variables were included in clinic-based models using co-kriging methods to assess whether cofactors improve clinic-based spatial HIV prevalence predictions. Clinic- and population-based smoothed prevalence maps were compared using partial rank correlation coefficients and residual local indicators of spatial autocorrelation. Results: Routinely-collected clinic-based data captured most of the geographical heterogeneity described by population-based data but failed to detect some pockets of high prevalence. Analyses indicated that clinic-based data could accurately predict the spatial location of so-called HIV ‘hotspots’ in &gt; 50% of the high HIV burden areas. Conclusion: Clinic-based data can be used to accurately map the broad spatial structure of HIV prevalence and to identify most of the areas where the burden of the infection is concentrated (HIV ‘hotspots’). Where population-based data are not available, HIV data collected from health facilities may provide a second-best option to generate valid spatial prevalence estimates for geographical targeting and resource allocation

Social closeness can help, harm and be irrelevant in solving pure coordination problems

Experimental research has shown that ordinary people often perform remarkably well in solving coordination games that involve no conflicts of interest. While most experiments in the past studied such coordination games among socially distant anonymous players, here we study behaviour in a set of two player coordination games and compare the outcomes depending on whether the players are socially close or socially distant. We find that social closeness influences prospects for coordination, but whether it helps, harms, or has no impact on coordination probabilities, depends on the structure of the game

Identifying Talent in Youth Sport: A Novel Methodology Using Higher-Dimensional Analysis.

Prediction of adult performance from early age talent identification in sport remains difficult. Talent identification research has generally been performed using univariate analysis, which ignores multivariate relationships. To address this issue, this study used a novel higher-dimensional model to orthogonalize multivariate anthropometric and fitness data from junior rugby league players, with the aim of differentiating future career attainment. Anthropometric and fitness data from 257 Under-15 rugby league players was collected. Players were grouped retrospectively according to their future career attainment (i.e., amateur, academy, professional). Players were blindly and randomly divided into an exploratory (n = 165) and validation dataset (n = 92). The exploratory dataset was used to develop and optimize a novel higher-dimensional model, which combined singular value decomposition (SVD) with receiver operating characteristic analysis. Once optimized, the model was tested using the validation dataset. SVD analysis revealed 60 m sprint and agility 505 performance were the most influential characteristics in distinguishing future professional players from amateur and academy players. The exploratory dataset model was able to distinguish between future amateur and professional players with a high degree of accuracy (sensitivity = 85.7%, specificity = 71.1%; p<0.001), although it could not distinguish between future professional and academy players. The validation dataset model was able to distinguish future professionals from the rest with reasonable accuracy (sensitivity = 83.3%, specificity = 63.8%; p = 0.003). Through the use of SVD analysis it was possible to objectively identify criteria to distinguish future career attainment with a sensitivity over 80% using anthropometric and fitness data alone. As such, this suggests that SVD analysis may be a useful analysis tool for research and practice within talent identification