scholarly journals Objective Simulated Bush Engagement Experience (OSBEE): A novel approach to promote rural clinical workforce.

2021 ◽  
Vol 6 (2) ◽  
pp. 94-96
Author(s):  
Y.G. Shamalee Wasana Jayarathne ◽  
Riitta Partanen ◽  
Jules Bennet
Keyword(s):  
Rural Health ◽  
Medical Schools ◽  
Clinical Placement ◽  
Australian Government ◽  
Medical Workforce ◽  
Domestic Students ◽  
Short Term ◽  
Novel Approach ◽  
Workforce Shortages

The mal-distributed Australian medical workforce continues to result in rural medical workforce shortages. In an attempt to increase rural medical workforce, the Australian Government has invested in the Rural Health Multidisciplinary Training (RHMT) program, involving 21 medical schools (RHMT program, 2020). This funding requires participating universities to ensure at least 25% of domestic students attend a year-long rural placement during their clinical years and 50% of domestic students experience a short-term rural clinical placement for at least four weeks.

scholarly journals Forecasting the Direction of Short-Term Crude Oil Price Changes with Genetic-Fuzzy Information Distribution

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xinyu Wang ◽  
Kegui Chen ◽  
Xueping Tan
Keyword(s):  
Crude Oil ◽  
Oil Price ◽  
Fuzzy Information ◽  
Information Distribution ◽  
Short Term ◽  
Distribution Method ◽  
Price Changes ◽  
Spot Prices ◽  
Price Series ◽  
Novel Approach

This paper proposes a novel approach to the directional forecasting problem of short-term oil price changes. In this approach, the short-term oil price series is associated with incomplete fuzzy information, and a new fused genetic-fuzzy information distribution method is developed to process such a fuzzy incomplete information set; then a feasible coding method of multidimensional information controlling points is adopted to fit genetic-fuzzy information distribution to time series forecasting. Using the crude oil spot prices of West Texas Intermediate (WTI) and Brent as sample data, the empirical analysis results demonstrate that the novel fused genetic-fuzzy information distribution method statistically outperforms the benchmark of logistic regression model in prediction accuracy. The results indicate that this new approach is effective in direction accuracy.

scholarly journals Understanding beverage taxation: Perspective on the Philadelphia Beverage Tax’s novel approach

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Ryan M. Kane ◽  
Vasanti S. Malik
Keyword(s):  
Policy Process ◽  
Empirical Studies ◽  
Health Impact ◽  
Short Term ◽  
Sugar Sweetened Beverage ◽  
Novel Approach ◽  
Global Trend ◽  
Health Impact Assessments ◽  
Political Entrepreneurs

Despite the growing global trend of sugar-sweetened beverage (SSB) taxes for their potential as an untapped source of revenue and as a public health boon, these legislative efforts remain controversial. Multiple articles have reviewed this trend in recent years from modeling of long-term impacts to short-term empirical studies, yet most comprehensive, long-term health impact assessments remain forthcoming. These multi-faceted efficacy studies combined with case-based assessments of the policy process, descriptive pieces highlighting unique features of the policy and reflective perspectives targeting unanswered questions create a comprehensive body of literature to help inform present and future legislative efforts. The passage of the Philadelphia Beverage tax required a mix of political entrepreneurs, timing and context; while uniquely employing a nonpublic health frame, specific earmarking and a broadened scope with the inclusion of diet beverages. This perspective on the Philadelphia Beverage Tax will describe the passage and novel features of the Philadelphia Beverage Tax with a discussion of the ethical questions unique to this case.

scholarly journals Rural Work and Specialty Choices of International Students Graduating from Australian Medical Schools: Implications for Policy

2019 ◽  
Vol 16 (24) ◽  
pp. 5056 ◽  
Author(s):  
Matthew R. McGrail ◽  
Belinda G. O’Sullivan ◽  
Deborah J. Russell
Keyword(s):  
International Students ◽  
Medical Schools ◽  
Medical Workforce ◽  
Medical Work ◽  
Policy Makers ◽  
Logistic Regression Models ◽  
Medical Graduates ◽  
National Medical ◽  
Rural Work ◽  
Domestic Medical

Almost 500 international students graduate from Australian medical schools annually, with around 70% commencing medical work in Australia. If these Foreign Graduates of Accredited Medical Schools (FGAMS) wish to access Medicare benefits, they must initially work in Distribution Priority Areas (mainly rural). This study describes and compares the geographic and specialty distribution of FGAMS. Participants were 18,093 doctors responding to Medicine in Australia: Balancing Employment and Life national annual surveys, 2012–2017. Multiple logistic regression models explored location and specialty outcomes for three training groups (FGAMS; other Australian-trained (domestic) medical graduates (DMGs); and overseas-trained doctors (OTDs)). Only 19% of FGAMS worked rurally, whereas 29% of Australia’s population lives rurally. FGAMS had similar odds of working rurally as DMGs (OR 0.93, 0.77–1.13) and about half the odds of OTDs (OR 0.48, 0.39–0.59). FGAMS were more likely than DMGs to work as general practitioners (GPs) (OR 1.27, 1.03–1.57), but less likely than OTDs (OR 0.74, 0.59–0.92). The distribution of FGAMS, particularly geographically, is sub-optimal for improving Australia’s national medical workforce goals of adequate rural and generalist distribution. Opportunities remain for policy makers to expand current policies and develop a more comprehensive set of levers to promote rural and GP distribution from this group.

The overlap of aftershock coda waves and forecasting the first hour aftershocks

2020 ◽  
Author(s):  
Eugenio Lippiello ◽  
Giuseppe Petrillo ◽  
Cataldo Godano ◽  
Lucilla de Arcangelis ◽  
Anna Tramelli ◽  
...  
Keyword(s):  
Aftershock Sequence ◽  
Coda Waves ◽  
Statistical Features ◽  
Short Term ◽  
Geophysical Research ◽  
Ground Shaking ◽  
Epidemic Type Aftershock Sequence ◽  
Novel Approach ◽  
Single Station ◽  
Strong Ground

<p>We show that short term post-seismic incompleteness can be interpreted in terms of the overlap of aftershock coda waves. We use this information to develop a novel procedure which gives accurate occurrence probabilities of post-seismic strong ground shaking within 30 minutes after the mainshock. This novel approach uses, as only information, the ground velocity recorded at a single station without requiring that signals are transferred and elaborated by operational units. We will also discuss how this information can be implemented in the Epidemic-Type Aftershock Sequence model in order to reproduce statistical features in time and magnitude of recorded aftershocks.</p><p><strong>Main references </strong></p><p>de Arcangelis L., Godano C. & Lippiello E. (2018) <em>The Overlap of Aftershock Coda Waves and Short-Term Postseismic Forecasting. </em><strong>Journal of Geophysical Research: Solid Earth, </strong>123: 5661-5674,doi:10.1029/2018JB015518</p><p>Lippiello E., Petrillo G. , Godano G. , Tramelli A., Papadimitriou E. &, Karakostas V. (2019)<em> Forecasting of the first hour aftershocks by means of the perceived magnitude. </em><strong>Nature Communications</strong> , 10, 2953, doi:10.1038/s41467-019-10763-3</p>

Grey system based novel approach for stock market forecasting

2015 ◽  
Vol 5 (2) ◽  
pp. 178-193 ◽  
Author(s):  
R.M. Kapila Tharanga Rathnayaka ◽  
D.M.K.N Seneviratna ◽  
Wei Jianguo
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Stock Exchange ◽  
Moving Average ◽  
Grey Prediction ◽  
Grey System ◽  
Short Term ◽  
Content Type ◽  
Stock Market Forecasting ◽  
Novel Approach

Purpose – Making decisions in finance have been regarded as one of the biggest challenges in the modern economy today; especially, analysing and forecasting unstable data patterns with limited sample observations under the numerous economic policies and reforms. The purpose of this paper is to propose suitable forecasting approach based on grey methods in short-term predictions. Design/methodology/approach – High volatile fluctuations with instability patterns are the common phenomenon in the Colombo Stock Exchange (CSE), Sri Lanka. As a subset of the literature, very few studies have been focused to find the short-term forecastings in CSE. So, the current study mainly attempted to understand the trends and suitable forecasting model in order to predict the future behaviours in CSE during the period from October 2014 to March 2015. As a result of non-stationary behavioural patterns over the period of time, the grey operational models namely GM(1,1), GM(2,1), grey Verhulst and non-linear grey Bernoulli model were used as a comparison purpose. Findings – The results disclosed that, grey prediction models generate smaller forecasting errors than traditional time series approach for limited data forecastings. Practical implications – Finally, the authors strongly believed that, it could be better to use the improved grey hybrid methodology algorithms in real world model approaches. Originality/value – However, for the large sample of data forecasting under the normality assumptions, the traditional time series methodologies are more suitable than grey methodologies; especially GM(1,1) give some dramatically unsuccessful results than auto regressive intergrated moving average in model pre-post stage.

A NOVEL APPROACH FOR SHORT-TERM LOAD FORECASTING USING SUPPORT VECTOR MACHINES

2004 ◽  
Vol 14 (05) ◽  
pp. 329-335 ◽  
Author(s):  
LIANG TIAN ◽  
AFZEL NOORE
Keyword(s):  
Neural Network ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Feed Forward Neural Network ◽  
Modeling Approach ◽  
Novel Approach ◽  
Power Load ◽  
Short Term Load Forecasting ◽  
Network Approaches

A support vector machine (SVM) modeling approach for short-term load forecasting is proposed. The SVM learning scheme is applied to the power load data, forcing the network to learn the inherent internal temporal property of power load sequence. We also study the performance when other related input variables such as temperature and humidity are considered. The performance of our proposed SVM modeling approach has been tested and compared with feed-forward neural network and cosine radial basis function neural network approaches. Numerical results show that the SVM approach yields better generalization capability and lower prediction error compared to those neural network approaches.

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