scholarly journals The determinants of patients’ opinions about physicians’ services in the Internet-based ranking site

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
P Smola ◽  
M Martuszewska ◽  
A Maciak ◽  
M Duplaga
Keyword(s):  
Medical Practice ◽  
Multivariate Regression ◽  
Key Factors ◽  
Multivariate Regression Model ◽  
Data Set ◽  
Final Data ◽  
Considerable Impact ◽  
Source Of Information ◽  
Satisfaction With Services

Abstract Background The rankings of physicians’ services constructed on the basis of the feedback from patients became a popular form of the appraisal of their work. The main objective of the study was the assessment of predictors of the patients’ satisfaction with services provided by physicians of selected specialities. Methods The opinions about physicians of three specialties (paediatricians (P), gynaecologists (G) and family medicine physicians (FM)) who accumulated the highest numbers of opinions, were extracted from the rankinglekarzy.pl website. Only 100 physicians of each specialty who were the most frequently scored were included. Apart from the scores on six individual criteria, the data reflecting the number of opinions (NOO), the type of medical practice (TMP), number of specialties (NS) and PhD title (PhD), as well as the location of the practice (LOP) were collected. The total score was calculated as the sum of scores assigned to individual criteria. Results The final data set consisted of 9482 opinions (4234 for P, 2057 for FM, and 3191 for G). The multivariate regression model revealed that odds of obtaining the maximum total score (MTS) depended on S, NOO, LOP, and TMP. G had higher odds of receiving the MTS than P and FM (OR, 95%CI, 0.75, 0.68-0.83, and 0.72, 0.64-0.82, respectively). MTS was less frequently received by physicians practicing in medical centres (0.56, 0.49-0.62) and hospitals (0.55; 0.44-0.70) than in private practices, but not by those practicing in more than one place (3.81, 2.78-4.21). Higher NOO was related to lower odds of receiving MTS (0.992; 0.990-0.995). Conclusions There are significant differences in patients’ assessment of services provided by physicians of three analysed specialties. The type of practice has a considerable impact on the satisfaction of patients. Interestingly, it seems also that the number of specialties and PhD title do not influence patients’ opinions about the quality of the medical doctor’s services. Key messages Internet-based rankings of physicians may be an important source of information about provided medical services. Key factors influencing patients’ opinions include specialty, the type and the location of medical practice, but not scientific a scientific title and number of specialities.

Comparing predictive performance of general regression neural network (GRNN) and hedonic regression model for factors affecting housing prices in “Pune-India”

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abhijat Arun Abhyankar ◽  
Harish Kumar Singla
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Multivariate Regression ◽  
Housing Prices ◽  
Predictive Performance ◽  
General Regression Neural Network ◽  
Multivariate Regression Model ◽  
Data Set ◽  
Content Type ◽  
General Regression

Purpose The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general regression neural network (GRNN) model of housing prices in “Pune-India.” Design/methodology/approach Data on 211 properties across “Pune city-India” is collected. The price per square feet is considered as a dependent variable whereas distances from important landmarks such as railway station, fort, university, airport, hospital, temple, parks, solid waste site and stadium are considered as independent variables along with a dummy for amenities. The data is analyzed using a hedonic type multivariate regression model and GRNN. The GRNN divides the entire data set into two sets, namely, training set and testing set and establishes a functional relationship between the dependent and target variables based on the probability density function of the training data (Alomair and Garrouch, 2016). Findings While comparing the performance of the hedonic multivariate regression model and PNN-based GRNN, the study finds that the output variable (i.e. price) has been accurately predicted by the GRNN model. All the 42 observations of the testing set are correctly classified giving an accuracy rate of 100%. According to Cortez (2015), a value close to 100% indicates that the model can correctly classify the test data set. Further, the root mean square error (RMSE) value for the final testing for the GRNN model is 0.089 compared to 0.146 for the hedonic multivariate regression model. A lesser value of RMSE indicates that the model contains smaller errors and is a better fit. Therefore, it is concluded that GRNN is a better model to predict the housing price functions. The distance from the solid waste site has the highest degree of variable senstivity impact on the housing prices (22.59%) followed by distance from university (17.78%) and fort (17.73%). Research limitations/implications The study being a “case” is restricted to a particular geographic location hence, the findings of the study cannot be generalized. Further, as the objective of the study is restricted to just to compare the predictive performance of two models, it is felt appropriate to restrict the scope of work by focusing only on “location specific hedonic factors,” as determinants of housing prices. Practical implications The study opens up a new dimension for scholars working in the field of housing prices/valuation. Authors do not rule out the use of traditional statistical techniques such as ordinary least square regression but strongly recommend that it is high time scholars use advanced statistical methods to develop the domain. The application of GRNN, artificial intelligence or other techniques such as auto regressive integrated moving average and vector auto regression modeling helps analyze the data in a much more sophisticated manner and help come up with more robust and conclusive evidence. Originality/value To the best of the author’s knowledge, it is the first case study that compares the predictive performance of the hedonic multivariate regression model with the PNN-based GRNN model for housing prices in India.

scholarly journals The Study on Factors Influencing Incomes of Laborers in Viet Nam: The Case at Industrial Parks, Economic Zones in Travinh Province

2020 ◽  
Vol 11 (5) ◽  
pp. 321
Author(s):  
Ha Hong Nguyen ◽  
Tuyen Thanh Nguyen
Keyword(s):  
Multivariate Regression ◽  
Working Environment ◽  
Primary Data ◽  
Industrial Park ◽  
Viet Nam ◽  
Multivariate Regression Model ◽  
Factors Affecting ◽  
Industrial Parks ◽  
Economic Zones

This study aims to solve the problem of raising incomes, improving the quality of life of Vietnamese workers in industrial parks and economic zones today, specifically in Tra Vinh province, Viet Nam. By the method of primary data collection of 300 employees working in enterprises in Long Duc Industrial Park located in Tra Vinh City; Co Chien Industrial Park located in Cang Long district and Dinh An economic zones located in Tra Cu district; using multivariate regression model; The study showed that there are 6 factors affecting the income of workers: the occupation of workers, working experience, the qualifications of workers, ethnicity, Religion and working environment. In particular, working experience, the qualifications of workers greatly affect the income of employees. From the research results, the author have proposed solutions to improve the income of workers, ensure social security and stabilize the lives of workers in the future.

scholarly journals MeshAndCollect: an automated multi-crystal data-collection workflow for synchrotron macromolecular crystallography beamlines

2015 ◽  
Vol 71 (11) ◽  
pp. 2328-2343 ◽  
Author(s):  
Ulrich Zander ◽  
Gleb Bourenkov ◽  
Alexander N. Popov ◽  
Daniele de Sanctis ◽  
Olof Svensson ◽  
...  
Keyword(s):  
Hierarchical Cluster ◽  
Data Sets ◽  
Macromolecular Crystallography ◽  
X Ray Diffraction ◽  
Data Set ◽  
X Ray ◽  
Partial Data ◽  
Final Data ◽  
Dispersion Technique

Here, an automated procedure is described to identify the positions of many cryocooled crystals mounted on the same sample holder, to rapidly predict and rank their relative diffraction strengths and to collect partial X-ray diffraction data sets from as many of the crystals as desired. Subsequent hierarchical cluster analysis then allows the best combination of partial data sets, optimizing the quality of the final data set obtained. The results of applying the method developed to various systems and scenarios including the compilation of a complete data set from tiny crystals of the membrane protein bacteriorhodopsin and the collection of data sets for successful structure determination using the single-wavelength anomalous dispersion technique are also presented.

AFL: Adaptive Federated Learning Based on Personalized Model and Adaptive Communication

2021 ◽  
Author(s):  
Xing Wu ◽  
Fei Xiang Liu ◽  
Yue Zhao ◽  
Ming Zhao
Keyword(s):  
Rapid Development ◽  
Key Factors ◽  
Local Data ◽  
Adaptive Communication ◽  
Data Set ◽  
Factors Affecting ◽  
Public Datasets ◽  
Deep Learning Model ◽  
Important Significance

Federated learning (FL) has given indications of being effective as a architecture for distributed machine learning, which can ensure the data security for each client and train the global deep learning model. Due to the rapid development of this technology, issues remain to be fully explored. Among them, the robustness of the system, privacy protection and communication efficiency are the key factors affecting quality of FL. Here we propose a new FL model based on personalized model and adaptive communication, called Adaptive Federated Learning (AFL). Our model mainly uses two mechanisms: a) Each client trains the personalized local model according to its own local data set; b) Adaptive chain communication mode is adopted in federation aggregation to reduce the time spent in synchronizing training result. A large number of experiments on two public datasets: MNIST and CIFAR10 show that our model is more accurate by over 5% compared with the FedAvg, and is also faster by over 10% compared with Chain-PPFL, which provides a very important significance in theory and practical production.

The importance of data revisions for statistical inference

2021 ◽  
Vol 66 (2) ◽  
pp. 7-24
Author(s):  
Paulina Ziembińska
Keyword(s):  
Forecast Horizon ◽  
Data Revisions ◽  
Process Procedures ◽  
Final Data ◽  
Considerable Impact ◽  
A Minor ◽  
The Impact ◽  
Total Impact ◽  
Modelling Process

The aim of the study is a quantitative analysis of revisions conducted by means of a new, real-time macroeconomic dataset for Poland, designed on the basis of the Statistical bulletin (Biuletyn statystyczny) published by Statistics Poland, covering the period from as early as 1995 until 2017. Polish data have positively verified a number of hypotheses concerning the impact of data revisions on the modelling process. Procedures assessing the properties of time series can yield widely discrepant results, depending on the extent to which the applied data have been revised. A comparison of the fitted ARIMA models for series of initial and final data demonstrates that the fitted models are similar for the majority of variables. In the cases where the form of the model is identical for both series, the coefficients retain their scale and sign. Most differences between coefficients result from a different structure of the fitted model, which causes differences in the autoregressive structure and can have a considerable impact on the ex ante inference. A prognostic experiment confirmed these observations. For a large number of variables, the total impact of revisions on the forecasting process exceeds 10%. Extreme cases, where the impact goes beyond 100%, or situations where data have a direct impact on the forecast sign, are also relatively frequent. Taking these results into account by forecasters could significantly improve the quality of their predictions. The forecast horizon has a minor impact on these conclusions. The article is a continuation of the author's work from 2017.

scholarly journals The relationship between farmers’ quality of life and their leadership competencies

2021 ◽  
Vol 2 (2) ◽  
pp. 50-72
Author(s):  
Suzanna Windon ◽  
Daniel Robotham
Keyword(s):  
Quality Of Life ◽  
Positive Association ◽  
Leadership Competencies ◽  
Online Data ◽  
Significant Positive Association ◽  
Data Set ◽  
Difficult Conversations ◽  
Final Data ◽  
The Relationship

This quantitative study sought to explore Pennsylvania farmers’ perceptions of their quality of life during their busiest farm season and its relationship with farmers’ self-leadership and ability to lead others’ competencies. The convenience, unrestricted, self-selecting, and chain-referral sampling approaches were used to collect online data. The final data set included responses from 59 farmers. The overall mean score for self-leadership competencies was 3.93 (SD = .48), ability to lead others’ competencies was 3.96 (SD = .50), and farmers’ quality of life was 3.49 (SD = .69). A significant positive association found between farmers’ quality of life and self-leadership competencies (r = .64 p = .001), and ability to lead others’ competencies (r = .24 p = .013). Approximately 43 % of the variance in overall farmers’ quality of life was explained by farmers’ self-leadership and ability to lead others’ competencies. Extension practitioners should develop a leadership program for farmers that will address the following areas: farmers’ work-life balance during busy season and difficult conversations with farm employees.

scholarly journals Bootstrap based goodness-of-fit tests for binary multivariate regression models

2021 ◽  
Author(s):  
Mareike van Heel ◽  
Gerhard Dikta ◽  
Roel Braekers
Keyword(s):  
Multivariate Regression ◽  
Goodness Of Fit ◽  
Real Data ◽  
Parametric Family ◽  
Multivariate Regression Model ◽  
Data Set ◽  
Goodness Of Fit Tests ◽  
Higher Dimensional ◽  
Input Variables ◽  
Family Based

AbstractWe consider a binary multivariate regression model where the conditional expectation of a binary variable given a higher-dimensional input variable belongs to a parametric family. Based on this, we introduce a model-based bootstrap (MBB) for higher-dimensional input variables. This test can be used to check whether a sequence of independent and identically distributed observations belongs to such a parametric family. The approach is based on the empirical residual process introduced by Stute (Ann Statist 25:613–641, 1997). In contrast to Stute and Zhu’s approach (2002) Stute & Zhu (Scandinavian J Statist 29:535–545, 2002), a transformation is not required. Thus, any problems associated with non-parametric regression estimation are avoided. As a result, the MBB method is much easier for users to implement. To illustrate the power of the MBB based tests, a small simulation study is performed. Compared to the approach of Stute & Zhu (Scandinavian J Statist 29:535–545, 2002), the simulations indicate a slightly improved power of the MBB based method. Finally, both methods are applied to a real data set.

Selection by Publication in Economics

2005 ◽  
Vol 55 (3) ◽  
pp. 255-269 ◽  
Author(s):  
András Simonovits
Keyword(s):  
Selection Process ◽  
Professional Standards ◽  
Publication Process ◽  
Entry Costs ◽  
Scientific Papers ◽  
Financial Interests ◽  
Source Of Information

According to the dominant view, the quality of individual scientific papers can be evaluated by the standard of the journal in which they are published. This paper attempts to demonstrate the limits of this view in the field of economics. According to our main findings, a publication frequently serves as a signal of high professional standards rather than as a source of information; referees and editors frequently reject good papers and accept bad ones; citation indices only partially balance the distortions deriving from the selection process; there are essential “entry costs” to the publication process. Moreover, financial interests of publishers may contradict scientific interests. As long as leading economists do not give voice to their dissatisfaction, there is no hope for any reform of the selection process.

scholarly journals Calling for improved quality in the registration of traditional Chinese medicine during the public health emergency: a survey of trial registries for COVID-19, H1N1, and SARS

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zhuoran Kuang ◽  
◽  
Xiaoyan Li ◽  
Jianxiong Cai ◽  
Yaolong Chen ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Secondary Outcome ◽  
Specific Information ◽  
Clinical Trial Registry ◽  
Data Set ◽  
Diagnosis Criteria

Abstract Objective To assess the registration quality of traditional Chinese medicine (TCM) clinical trials for COVID-19, H1N1, and SARS. Method We searched for clinical trial registrations of TCM in the WHO International Clinical Trials Registry Platform (ICTRP) and Chinese Clinical Trial Registry (ChiCTR) on April 30, 2020. The registration quality assessment is based on the WHO Trial Registration Data Set (Version 1.3.1) and extra items for TCM information, including TCM background, theoretical origin, specific diagnosis criteria, description of intervention, and outcomes. Results A total of 136 records were examined, including 129 severe acute respiratory syndrome coronavirus 2 (COVID-19) and 7 H1N1 influenza (H1N1) patients. The deficiencies in the registration of TCM clinical trials (CTs) mainly focus on a low percentage reporting detailed information about interventions (46.6%), primary outcome(s) (37.7%), and key secondary outcome(s) (18.4%) and a lack of summary result (0%). For the TCM items, none of the clinical trial registrations reported the TCM background and rationale; only 6.6% provided the TCM diagnosis criteria or a description of the TCM intervention; and 27.9% provided TCM outcome(s). Conclusion Overall, although the number of registrations of TCM CTs increased, the registration quality was low. The registration quality of TCM CTs should be improved by more detailed reporting of interventions and outcomes, TCM-specific information, and sharing of the result data.

Simple Index to Assess the Calibration Quality of Safety Performance Functions Based on Multiple Goodness-of-Fit Metrics

2021 ◽  
pp. 036119812110088
Author(s):  
Raul E. Avelar ◽  
Karen Dixon ◽  
Boniphace Kutela ◽  
Sam Klump ◽  
Beth Wemple ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Synthetic Data ◽  
Calibration Procedure ◽  
Safety Performance ◽  
Absolute Deviation ◽  
Data Set ◽  
Safety Database ◽  
Simple Index ◽  
Safety Performance Functions

The calibration of safety performance functions (SPFs) is a mechanism included in the Highway Safety Manual (HSM) to adjust SPFs in the HSM for use in intended jurisdictions. Critically, the quality of the calibration procedure must be assessed before using the calibrated SPFs. Multiple resources to aid practitioners in calibrating SPFs have been developed in the years following the publication of the HSM 1st edition. Similarly, the literature suggests multiple ways to assess the goodness-of-fit (GOF) of a calibrated SPF to a data set from a given jurisdiction. This paper uses the calibration results of multiple intersection SPFs to a large Mississippi safety database to examine the relations between multiple GOF metrics. The goal is to develop a sensible single index that leverages the joint information from multiple GOF metrics to assess overall quality of calibration. A factor analysis applied to the calibration results revealed three underlying factors explaining 76% of the variability in the data. From these results, the authors developed an index and performed a sensitivity analysis. The key metrics were found to be, in descending order: the deviation of the cumulative residual (CURE) plot from the 95% confidence area, the mean absolute deviation, the modified R-squared, and the value of the calibration factor. This paper also presents comparisons between the index and alternative scoring strategies, as well as an effort to verify the results using synthetic data. The developed index is recommended to comprehensively assess the quality of the calibrated intersection SPFs.

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