The ethical intricacies of injury rehabilitation within a dance training context

Exploring the ethical issues present in professional practice within the field of sport, exercise and performance psychology, this case study outlines challenges that may be encountered, ways to address issues should they arise, and the overall ethical considerations of supporting injury rehabilitation within a dance training context.

Proactive training system for safe and efficient precast installation

The construction industry is a crucial component of the Hong Kong economy, and the safety and efficiency of workers are two of its main concerns. The current approach to training workers relies primarily on instilling practice and experience in conventional teacher-apprentice settings on and off site. Both have their limitations however, on-site training is very inefficient and interferes with progress on site, while off-site training provides little opportunity to develop the practical skills and awareness needed through hands-on experience. A more effective way is to train workers in safety awareness and efficient working by current novel information technologies. This paper describes a new and innovative prototype system – the Proactive Construction Management System (PCMS) – to train precast installation workers to be highly productive while being fully aware of the hazards involved. PCMS uses Chirp-Spread-Spectrum-based (CSS) real-time location technology and Unity3D-based data visualisation technology to track construction resources (people, equipment, materials, etc.) and provide real-time feedback and post-event visualisation analysis in a training environment. A trial of a precast facade installation on a real site demonstrates the benefits gained by PCMS in comparison with equivalent training using conventional methods. It is concluded that, although the study is based on specific industrial conditions found in Hong Kong construction projects, PCMS may well attract wider interest and use in future.

A randomized controlled trial of a program to reduce adolescent road-related medically-treated injuries

Background Injury is the leading cause of adolescent death and injury around the road is a common source of adolescent injuries. Skills for Preventing Injury in Youth (SPIY) is a comprehensive program developed in Australia for early adolescents (term-long curriculum, including looking out for friends, first-aid training coupled with teacher school-connectedness professional development). Jessors’ Protection-Risk Framework guided the program approach focusing on building protective relationships. Method A randomized controlled trial with 35 schools was undertaken. Students completed surveys at baseline, six-months post-intervention and twelve-months post intervention. There were 1686 students (56% female) who completed the twelve-month survey, including the Extended-Adolescent Injury Checklist whereby students self-report on medically-treated injuries over the previous three-months (only road-related items are reported in this study; cycling, motorcycle riding, pedestrian, and riding as a passenger). Randomly selected SPIY classes also participated in focus groups and reported on perceptions of SPIY and injury risk behavior. Results As a check of randomization baseline differences of the variables were examined, with no significant differences between intervention and control groups. At the 12-month follow-up, there were fewer medically-treated injuries among the intervention students compared with the control group, particularly associated with being a passenger. The process evaluation revealed students perceived change in injury risk and risk behaviors. Conclusions While data analyses are continuing, the results indicate that the program seeking to encourage adolescents to look out for their friends, build connections to school and provide first aid skills training goes some way to reducing self-reported medically-treated injuries around the road.

Regressive measures won’t help youth into work or training

The 2014 federal budget implemented a so-called crackdown on what Minister for Social Services Kevin Andrews calls young people who are content to “sit on the couch at home and pick up a welfare cheque”. The crackdown will change access to income support for people under 30 years of age. From January 1 2015, all young people seeking Newstart Allowance and Youth Allowance for the first time will be required to “demonstrate appropriate job search and participation in employment services support for six months before receiving payments”. Upon qualifying, recipients must then spend 25 hours per week in Work for the Dole in order to receive income support for a six-month period. What happens beyond this six months is unclear. What is clear is that these policy changes, together with the Minister’s accompanying statements, are informed by a deficit view of disadvantaged youth. It is a view that demonstrates how little politicians know or understand about these young peoples’ past circumstances.

How do experts think about statistics? Hints for improving undergraduate and postgraduate training

Experts are increasingly being called upon to quantify their knowledge, particularly in situations where data is not yet available or of limited relevance. In many cases this involves asking experts to estimate probabilities. For example experts, in ecology or related fields, might be called upon to estimate probabilities of incidence or abundance of species, and how they relate to environmental factors. Although many ecologists undergo some training in statistics at undergraduate and postgraduate levels, this does not necessarily focus on interpretations of probabilities. More accurate elicitation can be obtained by training experts prior to elicitation, and if necessary tailoring elicitation to address the expert’s strengths and weaknesses. Here we address the first step of diagnosing conceptual understanding of probabilities. We refer to the psychological literature which identifies several common biases or fallacies that arise during elicitation. These form the basis for developing a diagnostic questionnaire, as a tool for supporting accurate elicitation, particularly when several experts or elicitors are involved. We report on a qualitative assessment of results from a pilot of this questionnaire. These results raise several implications for training experts, not only prior to elicitation, but more strategically by targeting them whilst still undergraduate or postgraduate students.

The QUT Training Model for Peer Leader Capacity Building

QUT has enacted a university-wide Peer Program’s Strategy which aims to improve student success and graduate outcomes. A component of this strategy is a training model providing relevant, quality-assured and timely training for all students who take on leadership roles. The training model is designed to meet the needs of the growing scale and variety of peer programs, and to recognise the multiple roles and programs in which students may be involved during their peer leader journey. The model builds peer leader capacity by offering centralised, beginning and ongoing training modules, delivered by in-house providers, covering topics which prepare students to perform their role safely, inclusively, accountably and skilfully. The model also provides efficiencies by differentiating between ‘core competency' and ‘program-specific’ modules, thus avoiding training duplication across multiple programs, and enabling training to be individually and flexibly formatted to suit the specific and unique needs of each program.

Centroid training to achieve effective text classification

Traditional text classification technology based on machine learning and data mining techniques has made a big progress. However, it is still a big problem on how to draw an exact decision boundary between relevant and irrelevant objects in binary classification due to much uncertainty produced in the process of the traditional algorithms. The proposed model CTTC (Centroid Training for Text Classification) aims to build an uncertainty boundary to absorb as many indeterminate objects as possible so as to elevate the certainty of the relevant and irrelevant groups through the centroid clustering and training process. The clustering starts from the two training subsets labelled as relevant or irrelevant respectively to create two principal centroid vectors by which all the training samples are further separated into three groups: POS, NEG and BND, with all the indeterminate objects absorbed into the uncertain decision boundary BND. Two pairs of centroid vectors are proposed to be trained and optimized through the subsequent iterative multi-learning process, all of which are proposed to collaboratively help predict the polarities of the incoming objects thereafter. For the assessment of the proposed model, F1 and Accuracy have been chosen as the key evaluation measures. We stress the F1 measure because it can display the overall performance improvement of the final classifier better than Accuracy. A large number of experiments have been completed using the proposed model on the Reuters Corpus Volume 1 (RCV1) which is important standard dataset in the field. The experiment results show that the proposed model has significantly improved the binary text classification performance in both F1 and Accuracy compared with three other influential baseline models.

The impact of intense training on endogenous estrogen and progesterone concentrations and bone mineral acquisition in adolescent rowers

The effect of 18 months of training on the ovarian hormone concentrations and bone mineral density (BMD) accrual was assessed longitudinally in 14 adolescent rowers and 10 matched controls, aged 14–15 years. Ovarian hormone levels were assessed by urinary estrone glucuronide (E1G) and pregnanediol glucuronide (PdG) excretion rates, classifying the menstrual cycles as ovulatory or anovulatory. Total body (TB), total proximal femur (PF), femoral neck (FN) and lumbar spine (LS) (L2–4) bone mass were measured at baseline and 18 months using dual-energy X-ray densitometry. Results were expressed as bone mineral content (BMC), BMD and bone mineral apparent density (BMAD). Five rowers had anovulatory menstrual cycles compared with zero prevalence for the control subjects. Baseline TB BMD was significantly higher in the ovulatory rowers, with PF BMD, FN BMD and LS BMD similar for all groups. At completion, the LS bone accrual of the ovulatory rowers was significantly greater (BMC 8.1%, BMD 6.2%, BMAD 6.2%) than that of the anovulatory rowers (BMC 1.1%, BMD 3.9%, BMAD 1.6%) and ovulatory controls (BMC 0.5%, BMD 1.1%, BMAD 1.1%). No difference in TB, PF or FN bone accrual was observed among groups. This study demonstrated an osteogenic response to mechanical loading, with the rowers accruing greater bone mass than the controls at the lumbar spine. However, the exercise-induced osteogenic benefits were less when rowing training was associated with low estrogen and progesterone metabolite excretion.

Flight-training effect on the cervical muscle isometric strength of trainee pilots

External stimulus/loading initiates adaptations within skeletal muscle. It has been previously found that the cervical area has the highest loading while performing flying maneuvers under +Gz. The first purpose of this study was to examine the neck muscle response to the physical environment associated with flight training, incorporating limited exposure to +Gz force, in a Pilatus PC-9 aircraft. The second purpose was to examine the short-term range of movement (ROM) response to flight training. Isometric cervical muscle strength and ROM was monitored in 9 RAAF pilots completing an 8-mo flight-training course at Pearce Airbase in Western Australia, and in 10 controls matched for gender, age, height, and weight. Isometric cervical muscle strength and ROM were measured at baseline and at 8 mo using the multi-cervical rehabilitation unit (Hanoun Medical, Downsview, Ontario, Canada). Results indicated that an increase in pilot neck strength was limited to flexion while in a neutral position. No strength changes were recorded in any other site in the pilots or for the controls. These findings suggest that short-term exposure to the physical environment associated with flight training had a limited significant effect on increasing isometric cervical muscle strength. No significant changes were observed in pilot ROM, indicating that short-term exposure to flight does not effect ROM.

Changes in cervical spine bone mineral density in response to flight training

BACKGROUND High magnitude loads and unusual loading regimes are two important determinants for increasing bone mass. Past research demonstrated that positive Gz-induced loading, providing high loads in an unaccustomed manner, had an osteogenic effect on bone. Another determinant of bone mass is that the bone response to loading is site specific. This study sought to further investigate the site specific bone response to loading, examining the cervical spine response, the site suspected of experiencing the greatest loading, to high performance flight. METHODS Bone mineral density (BMD) and bone mineral content (BMC) was monitored in 9 RAAF trainee fighter pilots completing an 8-mo flight training course on a PC-9 and 10 age-height-weight-matched controls. RESULTS At completion of the course, the pilots had a significant increase in cervical spine BMD and total body BMC. No significant changes were found for the control group. CONCLUSIONS This study demonstrated that the physical environment associated with flight training may have contributed to a significant increase in cervical spine bone mass in the trainee PC-9 pilots. The increase in bone mass was possibly a response to the strain generated by the daily wearing of helmet and mask assembly under the influence of positive sustained accelerative forces.

Place recognition with ConvNet landmarks: Viewpoint-robust, condition-robust, training-free

Place recognition has long been an incompletely solved problem in that all approaches involve significant compromises. Current methods address many but never all of the critical challenges of place recognition – viewpoint-invariance, condition-invariance and minimizing training requirements. Here we present an approach that adapts state-of-the-art object proposal techniques to identify potential landmarks within an image for place recognition. We use the astonishing power of convolutional neural network features to identify matching landmark proposals between images to perform place recognition over extreme appearance and viewpoint variations. Our system does not require any form of training, all components are generic enough to be used off-the-shelf. We present a range of challenging experiments in varied viewpoint and environmental conditions. We demonstrate superior performance to current state-of-the- art techniques. Furthermore, by building on existing and widely used recognition frameworks, this approach provides a highly compatible place recognition system with the potential for easy integration of other techniques such as object detection and semantic scene interpretation.