CoachNet: The further development of a coordinated network for sport coaching in Europe

Leeds Metropolitan University (LMU), in partnership with the European Coaching Council (ECC), was successful in a bid to the European Commission under the Preparatory Action in the Field of Sport (EAC/18/2011). The project was designed to develop an innovative approach that would contribute to the strengthening of the organisation of sport in Europe as part of the ‘good governance, strand of the EU Preparatory Action in the Field of Sport. The primary objective was to examine ways in which the organisation of coaching could be enhanced in Europe, with a particular focus on the greater involvement of coaches in decisionmaking. In exploring ways to maximise the ‘voice of the coach’, the partnership between LMU and ECC was central to the project. ECC is the continental division of the International Council for Coaching Excellence (ICCE). Through its network, ECC was in a position to identify current organisational arrangements for coaching across Europe. LMU is a well established research and practice oriented university in the UK and played a lead role in coordinating the project and guiding the research methodology through its Sport Coaching and Physical Education (SCOPE) Research and Enterprise Centre. Varying arrangements for the development and management of coaching were observed through a study of European countries. Within this varied landscape, the representation of coaches was sporadic, ranging from no representative mechanism to a number of good practice examples that made provision for the tiered engagement of coaches depending on their role; sport and coaching status category. These examples included confederated models across sports; blended models across coaching status categories and single and multi-sport models for the engagement and representation of coaches. The study concluded that there is a need for a more considered approach to the involvement of coaches in decision-making, with a number of recommendations developed for consideration by member states and the European divisions of the International Federations. These recommendations proposed that the structure of ECC as the European arm of ICCE be reviewed, with the intention to more strongly engage organisations that have been established to represent the voice of coaches and leading to a re-structuring of the organisation. In this context, ICCE and ECC should play an even stronger advocacy, representative and action role in establishing coaching as a blended profession, which includes volunteer, part-time paid and full-time paid coaches. More coherent structures for the engagement of coaches in each sport and country are also recommended. This should occur as part of a wider commitment that the principle of listening to and hearing the voice of the coach should become more strongly embedded within the way in which sporting and related organisations operate. The EU is well placed to lead on this type of approach, ensuring the coaches are more fully engaged in social dialogue and in the process to further enhance the role of sport and coaching in Europe. Further research is also recommended on the nature, needs and demographics of the coaching workforce. All of these approaches need to be tempered with the realisation that coaches are individual decision-makers, operating in a wide variety of contexts and many of whom do not show a propensity for involvement in formal ‘representative’ structures. The need for alternative methods to connect with and engage coaches was, therefore, identified. These include a more segmented approach to engaging with coaches, depending on their coaching role and status, as well as the utilisation of more informal modes of web-based communication to connect directly with coaches in their daily lives. In all existing and future scenarios, the key role of federations at the national and international level in seeking, activating and allocating financial and other resources to connect with and support their coaches was highlighted. The findings have been notified to ICCE for formal consideration, leading to changes in the ways in which the voice of the coach is more clearly represented within the work of the organisation. ICCE should continue to work closely with the EU Sport Unit to ensure that the recommendations of this report are implemented and evaluated on an on-going basis

Can music move people? : The effects of musical complexity and silence on waiting time

Previous research has suggested that music might influence the amount of time for which people are prepared to wait in a given environment. In an attempt to investigate the mechanisms underlying such effects, this study employed three levels of musical complexity and also a “no-music” condition. While one of these played in the background, participants were left to wait in a laboratory for the supposed start of an experiment. The results indicated that participants waited for the least amount of time during the no-music condition, and that there were no differences between the three music conditions. Other evidence indicated that this may be attributable to the music distracting participants’ attention from an internal timing mechanism. The results are discussed in terms of their implications for consumer behavior and research on the psychology of everyday life

Vegetation height products between 60° S and 60° N from ICESat GLAS data.

We present new coarse resolution (0.5� ×0.5�)vegetation height and vegetation-cover fraction data sets between 60� S and 60� N for use in climate models and ecological models. The data sets are derived from 2003–2009 measurements collected by the Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat), the only LiDAR instrument that provides close to global coverage. Initial vegetation height is calculated from GLAS data using a development of the model of Rosette et al. (2008) with further calibration on desert sites. Filters are developed to identify and eliminate spurious observations in the GLAS data, e.g. data that are affected by clouds, atmosphere and terrain and as such result in erroneous estimates of vegetation height or vegetation cover. Filtered GLAS vegetation height estimates are aggregated in histograms from 0 to 70m in 0.5m intervals for each 0.5�×0.5�. The GLAS vegetation height product is evaluated in four ways. Firstly, the Vegetation height data and data filters are evaluated using aircraft LiDAR measurements of the same for ten sites in the Americas, Europe, and Australia. Application of filters to the GLAS vegetation height estimates increases the correlation with aircraft data from r =0.33 to r =0.78, decreases the root-mean-square error by a factor 3 to about 6m (RMSE) or 4.5m (68% error distribution) and decreases the bias from 5.7m to −1.3 m. Secondly, the global aggregated GLAS vegetation height product is tested for sensitivity towards the choice of data quality filters; areas with frequent cloud cover and areas with steep terrain are the most sensitive to the choice of thresholds for the filters. The changes in height estimates by applying different filters are, for the main part, smaller than the overall uncertainty of 4.5–6m established from the site measurements. Thirdly, the GLAS global vegetation height product is compared with a global vegetation height product typically used in a climate model, a recent global tree height product, and a vegetation greenness product and is shown to produce realistic estimates of vegetation height. Finally, the GLAS bare soil cover fraction is compared globally with the MODIS bare soil fraction (r = 0.65) and with bare soil cover fraction estimates derived from AVHRR NDVI data (r =0.67); the GLAS treecover fraction is compared with the MODIS tree-cover fraction (r =0.79). The evaluation indicates that filters applied to the GLAS data are conservative and eliminate a large proportion of spurious data, while only in a minority of cases at the cost of removing reliable data as well. The new GLAS vegetation height product appears more realistic than previous data sets used in climate models and ecological models and hence should significantly improve simulations that involve the land surface

Does pre-ordering tests enhance the value of the periodic examination? Study Design - Process implementation with retrospective chart review

<p>Abstract</p> <p>Background</p> <p>To evaluate the value of a pre-ordering process for the pro-active scheduling and completion of appropriate preventive and chronic disease monitoring tests prior to a periodic health examination (PHE).</p> <p>Methods</p> <p>A standardized template was developed and used by our nursing staff to identify and schedule appropriate tests prior to the patients PHE. Chart reviews were completed on all 602 PHE visits for a 3-month interval in a primary care setting. A patient satisfaction survey was administered to a convenience sample of the PHE patients.</p> <p>Results</p> <p>Of all the patients with tests pre-ordered, 87.8% completed the tests. All providers in the division used the process, but some evolved from one template to another over time. Most patients (61%) preferred to get their tests done prior to their PHE appointment. Many of our patients had abnormal test results. With this process, patients were able to benefit from face-to-face discussion of these results directly with their provider.</p> <p>Conclusions</p> <p>A pre-order process was successfully implemented to improve the value of the PHE visit in an internal medicine primary care practice using a standardized approach that allowed for provider autonomy. The process was accepted by patients and providers and resulted in improved office efficiency through reduced message handling. Completion of routine tests before the PHE office visit can help facilitate face-to-face discussions about abnormal results and subsequent management that otherwise may only occur by telephone.</p

Exploration and confirmation of factors associated with uncomplicated pregnancy in nulliparous women: prospective cohort study

Objective: To identify factors at 15 and 20 weeks’ gestation associated with a subsequent uncomplicated pregnancy. Design: Prospective international multicentre observational cohort study. Setting: Auckland, New Zealand and Adelaide, Australia (exploration and local replication dataset) and Manchester, Leeds, and London, United Kingdom, and Cork, Republic of Ireland (external confirmation dataset). Participants: 5628 healthy nulliparous women with a singleton pregnancy. Main outcome measure: Uncomplicated pregnancy, defined as a normotensive pregnancy delivered at >37 weeks’ gestation, resulting in a liveborn baby not small for gestational age, and the absence of any other significant pregnancy complications. In a stepwise logistic regression the comparison group was women with a complicated pregnancy. Results: Of the 5628 women, 3452 (61.3%) had an uncomplicated pregnancy. Factors that reduced the likelihood of an uncomplicated pregnancy included increased body mass index (relative risk 0.74, 95% confidence intervals 0.65 to 0.84), misuse of drugs in the first trimester (0.90, 0.84 to 0.97), mean diastolic blood pressure (for each 5 mm Hg increase 0.92, 0.91 to 0.94), and mean systolic blood pressure (for each 5 mm Hg increase 0.95, 0.94 to 0.96). Beneficial factors were prepregnancy fruit intake at least three times daily (1.09, 1.01 to 1.18) and being in paid employment (per eight hours’ increase 1.02, 1.01 to 1.04). Detrimental factors not amenable to alteration were a history of hypertension while using oral contraception, socioeconomic index, family history of any hypertensive complications in pregnancy, vaginal bleeding during pregnancy, and increasing uterine artery resistance index. Smoking in pregnancy was noted to be a detrimental factor in the initial two datasets but did not remain in the final model. Conclusions: This study identified factors associated with normal pregnancy through adoption of a novel hypothesis generating approach, which has shifted the emphasis away from adverse outcomes towards uncomplicated pregnancies. Although confirmation in other cohorts is necessary, this study implies that individually targeted lifestyle interventions (normalising maternal weight, increasing prepregnancy fruit intake, reducing blood pressure, stopping misuse of drugs) may increase the likelihood of normal pregnancy outcomes.Lucy C Chappell, Paul T Seed, Jenny Myers, Rennae S Taylor, Louise C Kenny, Gustaaf A Dekker, James J Walker, Lesley M E McCowan, Robyn A North, Lucilla Posto

Visual interaction with dimensionality reduction: a structured literature analysis

Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR with interactive visualizations. Nevertheless, a general, structured understanding of this integration is missing. To address this, we systematically studied the visual analytics and visualization literature to investigate how analysts interact with automatic DR techniques. The results reveal seven common interaction scenarios that are amenable to interactive control such as specifying algorithmic constraints, selecting relevant features, or choosing among several DR algorithms. We investigate specific implementations of visual analysis systems integrating DR, and analyze ways that other machine learning methods have been combined with DR. Summarizing the results in a “human in the loop” process model provides a general lens for the evaluation of visual interactive DR systems. We apply the proposed model to study and classify several systems previously described in the literature, and to derive future research opportunities

Human-centered machine learning through interactive visualization

The goal of visual analytics (VA) systems is to solve complex problems by integrating automated data analysis methods, such as machine learning (ML) algorithms, with interactive visualizations. We propose a conceptual framework that models human interactions with ML components in the VA process, and makes the crucial interplay between automated algorithms and interactive visualizations more concrete. The framework is illustrated through several examples. We derive three open research challenges at the intersection of ML and visualization research that will lead to more effective data analysis