The association of types of training and practice settings with doctors’ empathy and patient enablement among patients with chronic illness in Hong Kong

Background: The increase in non-communicable disease (NCD) is becoming a global health problem and there is an increasing need for primary care doctors to look after these patients although whether family doctors are adequately trained and prepared is unknown. Objective: This study aimed to determine if doctors with family medicine (FM) training are associated with enhanced empathy in consultation and enablement for patients with chronic illness as compared to doctors with internal medicine training or without any postgraduate training in different clinic settings. Methods: This was a cross-sectional questionnaire survey using the validated Chinese version of the Consultation and Relational Empathy (CARE) Measure as well as Patient Enablement Instrument (PEI) for evaluation of quality and outcome of care. 14 doctors from hospital specialist clinics (7 with family medicine training, and 7 with internal medicine training) and 13 doctors from primary care clinics (7 with family medicine training, and 6 without specialist training) were recruited. In total, they consulted 823 patients with chronic illness. The CARE Measure and PEI scores were compared amongst doctors in these clinics with different training background: family medicine training, internal medicine training and those without specialist training. Generalized estimation equation (GEE) was used to account for cluster effects of patients nested with doctors. <b>Results</b> Within similar clinic settings, FM trained doctors had higher CARE score than doctors with no FM training. In hospital clinics, the difference of the mean CARE score for doctors who had family medicine training (39.2, SD = 7.04) and internal medicine training (35.5, SD = 8.92) was statistically significant after adjusting for consultation time and gender of the patient. In the community care clinics, the mean CARE score for doctors with family medicine training and those without specialist training were 32.1 (SD = 7.95) and 29.2 (SD = 7.43) respectively, but the difference was not found to be significant. For PEI, patients receiving care from doctors in the hospital clinics scored significantly higher than those in the community clinics, but there was no significant difference in PEI between patients receiving care from doctors with different training backgrounds within similar clinic setting. Conclusion: Family medicine training was associated with higher patient perceived empathy for chronic illness patients in the hospital clinics. Patient enablement appeared to be associated with clinic settings but not doctors’ training background. Training in family medicine and a clinic environment that enables more patient doctor time might help in enhancing doctors’ empathy and enablement for chronic illness patients

Variability of organic and elemental carbon, water soluble organic carbon, and isotopes in Hong Kong

International audienceTo determine the levels and variations of carbonaceous aerosol in Hong Kong, PM2.5 and PM10 samples were collected by high volume (Hi-vol) samplers at three monitoring stations (representing middle-scale roadside, urban-, and regional-scale environments) during winter (November 2000 to February 2001) and summer (June 2001 to August 2001) periods. The highest concentrations of organic carbon (OC), elemental carbon (EC), and water-soluble organic carbon (WSOC) were found at the middle-scale roadside site with the lowest at the regional-scale site. The percentages of WSOC in total carbon at these sites were inversely correlated with their concentrations (i.e., the highest percentages of WSOC were observed at the regional-scale site). A high WSOC fraction may be associated with aged aerosol because of the secondary formation by photochemical oxidation of organic precursors of anthropogenic pollutants during transport. The annual average of isotope abundances (?13C) of OC and EC were ?26.9±0.5? and ?25.6±0.1?, respectively. There were no notable differences for seasonal distributions of carbon isotopic composition, consistent with motor vehicle emissions being the main source contributors of carbonaceous aerosol in Hong Kong. OC 13C abundances at the regional-scale site were higher than those at the middle-scale roadside and urban sites, consistent with secondary organic aerosols of biogenic origin

Global incidence and mortality for prostate cancer: analysis of temporal patterns and trends in 36 countries

Background: Prostate cancer (PCa) is a leading cause of mortality and morbidity globally, but its specific geographic patterns and temporal trends are under-researched. Objective: To test the hypotheses that PCa incidence is higher and PCa mortality is lower in countries with higher socioeconomic development, and that temporal trends for PCa incidence have increased while mortality has decreased over time. Design, setting, and participants: Data on age-standardized incidence and mortality rates in 2012 were retrieved from the GLOBOCAN database. Temporal patterns were assessed for 36 countries using data obtained from Cancer incidence in five continents volumes I–X and the World Health Organization mortality database. Correlations between incidence or mortality rates and socioeconomic indicators (human development index [HDI] and gross domestic product [GDP]) were evaluated. Outcome measurements and statistical analysis: The average annual percent change in PCa incidence and mortality in the most recent 10 yr according to join-point regression. Results and limitations: Reported PCa incidence rates varied more than 25-fold worldwide in 2012, with the highest incidence rates observed in Micronesia/Polynesia, the USA, and European countries. Mortality rates paralleled the incidence rates except for Africa, where PCa mortality rates were the highest. Countries with higher HDI (r = 0.58) and per capita GDP (r = 0.62) reported greater incidence rates. According to the most recent 10-yr temporal data available, most countries experienced increases in incidence, with sharp rises in incidence rates in Asia and Northern and Western Europe. A substantial reduction in mortality rates was reported in most countries, except in some Asian countries and Eastern Europe, where mortality increased. Data in regional registries could be underestimated. Conclusions: PCa incidence has increased while PCa mortality has decreased in most countries. The reported incidence was higher in countries with higher socioeconomic development. Patient summary: The incidence of prostate cancer has shown high variations geographically and over time, with smaller variations in mortality

Hybrid fuzzy modelling using memetic algorithm for hydrocyclone control

The use of a hybrid fuzzy modeling can act as a good alternative in establishing a hydrocyclone control model in estimating the hydrocyclone parameter, d50c. In most control and engineering applications, the use of fuzzy system as a way to improve the human-computer interaction has becoming popular. The main advantage of this proposed hybrid fuzzy system used for hydrocyclone control is that it only presents a small amount of fuzzy rules. It uses memetic algorithms to optimize the fuzzy parameters of the system to yield in a more accurate hydrocyclone control system

Reservoir characterization using support vector machines

Reservoir characterization especially well log data analysis plays an important role in petroleum exploration. This is the process used to identify the potential for oil production at a given source. In recent years, support vector machines (SVMs) have gained much attention as a result of its strong theoretical background. SVM is based on statistical learning theory known as the Vapnik-Chervonenkis theory. The theory has a strong mathematical foundation for dependencies estimation and predictive learning from finite data sets. This paper presents investigation on the use of SVM in reservoir characterization. Initial results show that SVM can be an alternative intelligent technique for reservoir characterization

Unplugged

Works were selected for this video screening to explore cross-cultural and / or environmental concerns, described by the organisers as "A consideration of 'symbiosis with nature' within contemporary art". Tadashi Kawamata’s The Watchtower, located in the grounds of the Museum, was used as the backdrop to the screening. During my piece, the surrounding crickets became the dominant sound and the audience gradually re-directed their gaze towards the full moon. The ‘silence’ lasted a minute, in concordance with Japanese tradition at times of mourning and / or remembrance. This work references John Cage’s 4minutes 33seconds. It is both a critical response to the premise of the video screening as outlined above, as well as a means of further exploring ideas of dependency in relation to ecological / sustainable ideals, i.e. this work (as an example of an ecologically sustainable art practice) could only exist, as such, because of the comparatively un-ecological, 'techno-rich' work of the other artists in this context

Gastric sarcoidosis presenting with haematemesis

Medical Journal of Australia2247-49MJAU

Intelligent well log data analysis for reservoir characterization

Well log data analysis plays an important role in petroleum exploration. It is used to identify the potential for oil production at a given source and so forms the basis for the estimation of financial returns and economic benefits. In recent years, many computational intelligence techniques such as backpropagation neural networks (BPNN) and fuzzy systems have been applied to perform the task. Support vector machines (SVMs) are new techniques and very few reports have been published in this application area. This paper presents the investigation and comparison of BPNN model with a SVM model on a set of practical well log data. Future directions of exploring of the use of SVM for improved results will also be discussed

An integrated intelligent technique for monthly rainfall time series prediction

This paper proposes a methodology to create an interpretable fuzzy model for monthly rainfall time series prediction. The proposed methodology incorporates the advantages of artificial neural network, fuzzy logic and genetic algorithm. In the first step, the differences between the time series data are calculated and they are used to define the interval between the membership functions of a Mamdani-type fuzzy inference system. Next, artificial neural network is used to develop the model from input-output data and the established model is then used to extract the fuzzy rules. The parameters of the created fuzzy model are then optimized by using genetic algorithm. The proposed model was applied to eight monthly rainfall time series data in the northeast region of Thailand. The experimental results showed that the proposed model provided satisfactory prediction accuracy when compared to other commonly-used prediction models. Due to the interpretability nature of the model, human analysts can gain insight knowledge of the data to be modeled