The aims of our awareness campaign were to increase the number of inquiries by patients to doctors for two new diabetes drugs funded by Pharmac on 1 February 2021 and 1 September 2021 respectively, to increase the number of applications for special authority, and to trial a "grass roots" community dissemination of information that appeals to explicit individual benefit from the new medicines. The campaign used an approach tailored primarily to the Pasifika community. The campaign ran from April 2021 to July 2021 and targeted Counties Manukau communities using a talanoa approach by primarily by sharing key messages informally through social networks face-to-face by word-of-mouth. The key messages about the new medicines were shared orally with local organisations, family, friends, influential community leaders and colleagues such as justices of peace, kapa haka leaders, committee representatives from local schools, sports, cultural and hobby clubs. A printed pamphlet translated in Māori, Samoan, Tongan and English with the key messages was also distributed widely. The campaign notified 102 primary care practices, used Pacific equity teams to disseminate the information, promoted the message on Māori and Pasifika radio stations, and engaged a public relations company who contacted the South Auckland Community Trust, councillors, community boards and local churches. This approach was intended to spread the message through the community to reach people with type 2 diabetes and/or their families to prompt them to contact their doctor and see if they are eligible. To gauge how effective the campaign was, we gathered data from Pharmac that quantified new prescriptions for the new medicines by location and ethnicity. An estimated 45,000 people were exposed to our campaign materials or were told about the new medicines by people they knew. These estimations were conservatively based on the known membership, listenership, and reach of the various delivery arms by which this campaign was delivered. These data show Pacific patients, the focus of about 64% of our project work, were 40% more likely to apply and receive a prescription for empagliflozin in Counties Manukau than anywhere else in the country. Direct-to-consumer marketing is an effective way of increasing health awareness and uptake of newly funded diabetes medicine amongst Pacific patients with type 2 diabetes.
To examine students' reasons for studying and their learning experiences in the Goodfellow Unit postgraduate distance learning diplomas. A survey was sent to all students currently enrolled in the Goodfellow Unit diplomas in emergency, sports and geriatric medicine. The response rate was 63%. Students had enrolled to gain more skills on their present job for personal satisfaction or for personal interest. Students reported difficulties in fitting study into already busy lives, but general satisfaction with the learning format of videotaped lecture sessions plus written study guides. They also reported having to develop a different method of learning from their undergraduate study methods. Students predicted a number of ways of improving learning outcomes. Distance learning is a viable option for busy general practitioners, as long as it can be self paced, practical and relevant to the work situation.
To assess the current state of knowledge around sport-related concussion (SRC) guidelines and management among primary care doctors in New Zealand. An online, self-administered, 21-item multi-choice questionnaire targeted at general practitioners and urgent care doctors in New Zealand was used. Main outcome measures were knowledge and management of patients with SRC through to return-to-sport. There were 230 total valid responses. Over half had no knowledge of the Consensus Statement on Concussion in Sport, and only 43% used the Sport Concussion Assessment Tool (SCAT) routinely. Fifty-eight percent would prefer to have a screening tool integrated into their patient management software. Most reported using appropriate management strategies for patients with concussion and recognised the potential benefit of relative cognitive and physical rest. There was low utilisation of referral pathways to allied health practitioners and specialist concussion services. Half (53%) felt confident in managing a patient with SRC and 46% felt comfortable managing return-to-sport. Primary care doctors have good knowledge of SRC but are not as confident managing return-to-sport. Further education opportunities were identified. Development of concussion tools adapted for use in primary care, integrated with patient management software and that support pathways to optimise patient recovery are recommended.
To assess the suitability of two previously unused data sources for monitoring rugby injury throughout New Zealand. Interviews were conducted with respondents sampled from players registered with the Rugby Football Unions (RFUs) and players claiming for rugby injuries from the Accident Rehabilitation and Compensation Insurance Corporation (ACC) in Auckland and Dunedin. Of the 500 RFU players sampled, 63% were interviewed and of these 39 (12%) had been injured playing rugby union. Of the 456 ACC claimants sampled, 66% were interviewed and 265 (88%) had been injured playing rugby union. Identifying injured players through ACC claims was more efficient, both procedurally and because a smaller sample size was required to detect changes in incidence. With no routine surveillance of sports injury being undertaken, recording sporting codes in national injury surveillance systems would assist the monitoring of sports injury.
To determine the prevalence of cigarette smoking in 14 and 15 year old school children in New Zealand and to examine associated risk factors. Nationwide cross-sectional survey of fourth-form school children in New Zealand by means of an anonymous self administered questionnaire in November 1992. Questionnaires from 14,097 fourteen and fifteen year-olds were analysed. 65.6% had tried smoking, and 36.1% regarded themselves as smokers. Females and Maori had significantly higher prevalence rates. Of Maori females 44.6% were current smokers (more than one per month) compared to 24.0% for the whole group, and 33.0% were daily smokers. Pacific Island students, who have similar socioeconomic disadvantage to Maori, have a lower relative risk of smoking (RR) 0.79, (95% confidence interval (CI) 0.68, 0.91), than Europeans. Major independent risk factors were identified and population attributable risk was calculated for parental smoking (22.9%), poor knowledge of adverse health effects (7.3%) and watching televised sports (13.4%). These three modifiable factors accounted for 36.1% of the total smoking prevalence in these children. The continued high prevalence of smoking in New Zealand children, especially in Maori and in females, prove current public health measures to be inadequate. Our results suggest that strategies aimed at decreasing parental smoking, improving student knowledge of adverse health effects and preventing tobacco sponsorship of television sports could greatly decrease the smoking prevalence in this age group.
To describe the biopsychosocial characteristics of a series of Pacific men living in South Auckland with a history of boxing presenting with early onset dementia. We discuss the history of boxing in Pacific people and the possibility of increased risk of early onset dementia in New Zealand Pacific men compared to their European counterparts. We reviewed the files of Pacific men with a history of amateur or professional boxing who presented to our memory and older adult mental health services with early onset dementia over a 45-month period. We gathered relevant information to construct a biopsychosocial paradigm as possible explanation of this phenomenon. We identified a series of eight New Zealand Pacific men with early onset dementia and with a history of boxing. Alcohol was a contributing factor in seven of the eight cases, and vascular risk factors in five. Historical, cultural and socio-economic factors underpin the attraction of some Pacific men to boxing as a sport. Given that New Zealand Pacific peoples may have an earlier onset of dementia than their European counterparts, further research is required to establish whether boxing is a contributory factor. Sports physicians should advise young New Zealand Pacific boxers about the long-term risks associated with their sport.
To assess the attitudes of mountain bikers to the use of protective equipment and quantify the use of such equipment. This was a prospective cohort study using an online questionnaire, offered to bikers participating in a series of Enduro races. The attitudes towards various factors that might contribute to a rider's choice to use protective equipment were quantified based on their responses to the questions. The actual reported use of various types of protective equipment was the outcome measure. The correlations between the factors and actual use were analysed for statistical significance, to assess their relative importance. Equipment use was similar in racing and non-racing settings and could be increased. 55% had experienced an injury requiring a week or more off work. Perceptions of the benefits, costs, cues, comfort and potential injury severity proved to be well correlated with the choice to use equipment, while harm, danger and exposure to media influences did not.
Coronary heart disease risk factors and circumstances of death were examined in 258 people who were a representative sample of all persons, less than 70 years of age, dying suddenly and unexpectedly in Auckland during the twelve month period from 1 March 1981. From this sample we identified nine individuals who had been running regularly for more than three months prior to death. All those identified were men aged between 35 and 56 years, all had historical evidence of abnormal coronary heart disease risk factors and six were known to have had symptomatic cardiovascular disease. Seven of the nine died during or within 30 minutes of exercise and eight of the nine had postmortems showing severe coronary artery disease.
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Current medical retrieval-augmented generation (RAG) approaches overlook evidence-based medicine (EBM) principles, leading to two key gaps: (1) the lack of PICO alignment between queries and retrieved evidence, and (2) the absence of evidence hierarchy considerations during reranking. We present SR-RAG, an EBM-adapted GraphRAG framework that integrates the PICO framework into knowledge graph construction and retrieval, and proposes Bayesian Evidence Tier Reranking (BETR) to calibrate ranking scores by evidence grade without predefined weights. Validated in sports rehabilitation, we release a knowledge graph (357,844 nodes, 371,226 edges) and a benchmark of 1,637 QA pairs. SR-RAG achieves 0.812 evidence recall@10, 0.830 nugget coverage, 0.819 answer faithfulness, 0.882 semantic similarity, and 0.788 PICOT match accuracy, substantially outperforming five baselines. Five expert clinicians rated the system 4.66--4.84 on a 5-point Likert scale, and system rankings are preserved on a human-verified gold subset (n=80).
Small Language Models (SLMs) have potential to be used for automatically labelling and identifying aspects of text data for medicine/health-related purposes from documents and the web. As their resource requirements are significantly lower than Large Language Models (LLMs), these can be deployed potentially on more types of devices. SLMs often are benchmarked on health/medicine-related tasks, such as MedQA, although performance on these can vary especially depending on the size of the model in terms of number of parameters. Furthermore, these test results may not necessarily reflect real-world performance regarding the automatic labelling or identification of texts in documents and the web. As a result, we compared topic-relatedness scores from Microsofts phi-3-mini-4k-instruct SLM to the topic-relatedness scores from 7 human evaluators on 1144 samples of medical/health-related texts and 1117 samples of sports injury-related texts. These texts were from a larger dataset of about 9 million news headlines, each of which were processed and assigned scores by phi-3-mini-4k-instruct. Our sample was selected (filtered) based on 1 (low filtering) or more (high filtering) Boolean condition
Understanding sports is crucial for the advancement of Natural Language Processing (NLP) due to its intricate and dynamic nature. Reasoning over complex sports scenarios has posed significant challenges to current NLP technologies which require advanced cognitive capabilities. Toward addressing the limitations of existing benchmarks on sports understanding in the NLP field, we extensively evaluated mainstream large language models for various sports tasks. Our evaluation spans from simple queries on basic rules and historical facts to complex, context-specific reasoning, leveraging strategies from zero-shot to few-shot learning, and chain-of-thought techniques. In addition to unimodal analysis, we further assessed the sports reasoning capabilities of mainstream video language models to bridge the gap in multimodal sports understanding benchmarking. Our findings highlighted the critical challenges of sports understanding for NLP. We proposed a new benchmark based on a comprehensive overview of existing sports datasets and provided extensive error analysis which we hope can help identify future research priorities in this field.
Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval. However, this task has not been explored due to the lack of relevant datasets and the challenging nature it presents. Most datasets for video question answering (VideoQA) focus mainly on general and coarse-grained understanding of daily-life videos, which is not applicable to sports scenarios requiring professional action understanding and fine-grained motion analysis. In this paper, we introduce the first dataset, named Sports-QA, specifically designed for the sports VideoQA task. The Sports-QA dataset includes various types of questions, such as descriptions, chronologies, causalities, and counterfactual conditions, covering multiple sports. Furthermore, to address the characteristics of the sports VideoQA task, we propose a new Auto-Focus Transformer (AFT) capable of automatically focusing on particular scales of temporal information for question answering. We conduct extensive experiments on Sports-QA, including baseline studies and the evaluation of different methods. The results demonstrate that our AFT achieves state-of-the-a
This chapter explores the complexities of sports governance, taxation, dispute resolution, and the impact of digital transformation within the sports sector. This study identifies a critical research gap regarding the integration of innovative technologies to enhance governance and talent identification in sports law. The objective is to evaluate how data-driven approaches and AI can optimize recruitment processes; also ensuring compliance with existing regulations. A comprehensive analysis of current governance structures and taxation policies,(ie Income Tax Act and GST Act), reveals preliminary results indicating that reform is necessary to support sustainable growth in the sports economy. Key findings demonstrate that AI enhances player evaluation by minimizing biases and expanding access to diverse talent pools. While the Court of Arbitration for Sport provides an efficient mechanism for dispute resolution. The implications emphasize the need for regulatory reforms that align taxation policies with international best practices, promoting transparency and accountability in sports organizations. This research contributes valuable insights into the evolving dynamics of sports mana
Sports game summarization aims at generating sports news from live commentaries. However, existing datasets are all constructed through automated collection and cleaning processes, resulting in a lot of noise. Besides, current works neglect the knowledge gap between live commentaries and sports news, which limits the performance of sports game summarization. In this paper, we introduce K-SportsSum, a new dataset with two characteristics: (1) K-SportsSum collects a large amount of data from massive games. It has 7,854 commentary-news pairs. To improve the quality, K-SportsSum employs a manual cleaning process; (2) Different from existing datasets, to narrow the knowledge gap, K-SportsSum further provides a large-scale knowledge corpus that contains the information of 523 sports teams and 14,724 sports players. Additionally, we also introduce a knowledge-enhanced summarizer that utilizes both live commentaries and the knowledge to generate sports news. Extensive experiments on K-SportsSum and SportsSum datasets show that our model achieves new state-of-the-art performances. Qualitative analysis and human study further verify that our model generates more informative sports news.
We present SMPLOlympics, a collection of physically simulated environments that allow humanoids to compete in a variety of Olympic sports. Sports simulation offers a rich and standardized testing ground for evaluating and improving the capabilities of learning algorithms due to the diversity and physically demanding nature of athletic activities. As humans have been competing in these sports for many years, there is also a plethora of existing knowledge on the preferred strategy to achieve better performance. To leverage these existing human demonstrations from videos and motion capture, we design our humanoid to be compatible with the widely-used SMPL and SMPL-X human models from the vision and graphics community. We provide a suite of individual sports environments, including golf, javelin throw, high jump, long jump, and hurdling, as well as competitive sports, including both 1v1 and 2v2 games such as table tennis, tennis, fencing, boxing, soccer, and basketball. Our analysis shows that combining strong motion priors with simple rewards can result in human-like behavior in various sports. By providing a unified sports benchmark and baseline implementation of state and reward des
Most sports visualizations rely on a combination of spatial, highly temporal, and user-centric data, making sports a challenging target for visualization. Emerging technologies, such as augmented and mixed reality (AR/XR), have brought exciting opportunities along with new challenges for sports visualization. We share our experience working with sports domain experts and present lessons learned from conducting visualization research in SportsXR. In our previous work, we have targeted different types of users in sports, including athletes, game analysts, and fans. Each user group has unique design constraints and requirements, such as obtaining real-time visual feedback in training, automating the low-level video analysis workflow, or personalizing embedded visualizations for live game data analysis. In this paper, we synthesize our best practices and pitfalls we identified while working on SportsXR. We highlight lessons learned in working with sports domain experts in designing and evaluating sports visualizations and in working with emerging AR/XR technologies. We envision that sports visualization research will benefit the larger visualization community through its unique challen
This paper explores the potential opportunities, risks, and challenges associated with the use of large language models (LLMs) in sports science and medicine. LLMs are large neural networks with transformer style architectures trained on vast amounts of textual data, and typically refined with human feedback. LLMs can perform a large range of natural language processing tasks. In sports science and medicine, LLMs have the potential to support and augment the knowledge of sports medicine practitioners, make recommendations for personalised training programs, and potentially distribute high-quality information to practitioners in developing countries. However, there are also potential risks associated with the use and development of LLMs, including biases in the dataset used to create the model, the risk of exposing confidential data, the risk of generating harmful output, and the need to align these models with human preferences through feedback. Further research is needed to fully understand the potential applications of LLMs in sports science and medicine and to ensure that their use is ethical and beneficial to athletes, clients, patients, practitioners, and the general public.
Camera virtualization -- an emerging solution to novel view synthesis -- holds transformative potential for visual entertainment, live performances, and sports broadcasting by enabling the generation of photorealistic images from novel viewpoints using images from a limited set of calibrated multiple static physical cameras. Despite recent advances, achieving spatially and temporally coherent and photorealistic rendering of dynamic scenes with efficient time-archival capabilities, particularly in fast-paced sports and stage performances, remains challenging for existing approaches. Recent methods based on 3D Gaussian Splatting (3DGS) for dynamic scenes could offer real-time view-synthesis results. Yet, they are hindered by their dependence on accurate 3D point clouds from the structure-from-motion method and their inability to handle large, non-rigid, rapid motions of different subjects (e.g., flips, jumps, articulations, sudden player-to-player transitions). Moreover, independent motions of multiple subjects can break the Gaussian-tracking assumptions commonly used in 4DGS, ST-GS, and other dynamic splatting variants. This paper advocates reconsidering a neural volume rendering fo