ABSTRACT
BACKGROUND: Child abuse is a significant issue across many countries. Despite the situation's innate understanding, many children are not reported to authorities and continue to experience abuse, sometimes even death. Healthcare professionals must be alert for abuse in any child who appears with injuries that are out of the ordinary because it is easy for indicators of child abuse to go unnoticed in a busy emergency department. The current study aims to evaluate and detect the challenges in diagnosing and reporting cases of child abuse among healthcare practitioners in emergency, pediatrics, and family medicine. METHODS: A self-administered online disseminated questionnaire was used for data collection during the period from October 1 to December 30, 2022. A cross-sectional study was conducted on emergency, pediatrics, and family medicine healthcare practitioners working in hospitals in healthcare centers in Riyadh, Saudi Arabia. All data were collected, tabulated, and statistically analyzed using SPSS 23.0 for (IBM Corp., Armonk, NY) Windows. RESULTS: The study sample constituted 200 physicians working in the front lines of healthcare like emergency, pediatrics, and family medicine primary care services, 50.5% were males and 49.5% were females. 36.5% of participants were 31-39 years old. 42% were family medicine physicians, 36.5% were pediatricians, and 21.5% were emergency medicine. About 43% of participants attended an educational workshop on child abuse. Nineteen percent of participants are very familiar with the diagnosis of child abuse and 36% of participants reported one to three cases of child abuse in the emergency department in the last year, 5% reported four to six cases and 56.5% reported none. Forty-seven percent of participants reported diagnosing one to five cases of child abuse throughout their whole career, 13% reported 11-15 cases, 6.5% reported six to 10 cases and 28.5% reported none. Causes of underdiagnosis of child abuse by healthcare providers were reported as 63% inexperience, 59% inadequate time for physical examination, 59% lack of diagnosis protocol, 51% lack of confidence in communicating with parents, 36% physicians' cultural background, and 38% lack of confidence in the diagnosis. 93.5% of participants think that healthcare practices need further education for child abuse. CONCLUSION: In conclusion, physicians in Saudi Arabia who participated in the study had good knowledge to diagnose a case of child abuse. Inexperience, inadequate time for physical examination, lack of diagnosis protocol, lack of confidence in communicating with parents, and physicians' cultural background were the main identified challenges for diagnosing child abuse. Familiarity with cases of child abuse was significantly associated with physicians' age, specialty, and level of training.
ABSTRACT
The two-parameter classical Weibull distribution is commonly implemented to cater for the product's reliability, model the failure rates, analyze lifetime phenomena, etc. In this work, we study a novel version of the Weibull model for analyzing real-life events in the sports and medical sectors. The newly derived version of the Weibull model, namely, a new cosine-Weibull (NC -Weibull) distribution. The importance of this research is that it suggests a novel version of the Wei-bull model without adding any additional parameters. Different distributional properties of the NC-Weibull distribution are obtained. The maximum likelihood approach is implemented to esti-mate the parameters of the NC-Weibull distribution. Finally, three applications are analyzed to prove the superiority of the NC-Weibull distribution over some other existing probability models considered in this study. The first and second applications, respectively, show the mortality rates of COVID-19 patients in Italy and Canada. Whereas, the third data set represents the injury rates of the basketball players collected during the 2008-2009 and 2018-2019 national basketball associ-ation seasons. Based on four selection criteria, it is observed that the NC-Weibull distribution may be a more suitable model for considering the sports and healthcare data sets.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/ 4.0/).
ABSTRACT
In this paper, we developed a novel superior distribution, demonstrated and derived its mathematical features, and assessed its fuzzy reliability function. The novel distribution has numerous advantages, including the fact that its CDf and PDf have a closed shape, making it particularly relevant in many domains of data science. We used both conventional and Bayesian approaches to make various sorts of estimations. A simulation research was carried out to investigate the performance of the classical and Bayesian estimators. Finally, we fitted a COVID-19 mortality real data set to the suggested distribution in order to compare its efficiency to that of its rivals. © 2022 Fathy H. Riad et al.
ABSTRACT
The aim of this work is to develop a new outstanding lifetime distribution, dubbed the power Bilal (PB) distribution. Both of the probability distribution function (pdf) and the cumlative distribution function (cdf) of the PB distribution have a simple forms. The suggested distribution's moments, incomplete moments as well as the quantile function are deduced and acquired in explicit forms as a result of its simple forms. Seven estimation methods for estimating the PB distribution parameters are mentioned, and numerical simulations are used to compare the proposed approaches using partial and overall ranks. According to the simulation results of this work, the maximum likelihood estimators are advised to be the best estimation method for estimating the parameters of the PB distribution, when the data genrated from the PB distribution. We shows the importance and flexibility of the PB distribution by comparing it to other existing competing distributions using two different real data sets from COVID-19 mortality rates of two countries. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/
ABSTRACT
In this paper, we introduce a new class of statistical models to deal with the data sets in the sports and health sectors. The new class is called, a novel exponent power-Y (NovEP-Y) family of distributions. By implementing the NovEP-Y approach, a new model, namely, a novel exponent power-Weibull (NovEP-Weibull) distribution is introduced. Some distributional properties of the NovEP-Y family such as identifiability, order statistics, quantile function, and moments are obtained. The maximum likelihood estimators of the parameters are also derived. Furthermore, a brief Monto Carlo simulation study is conducted to evaluate the performances of the estimators. To show the applicability of the NovEP-Weibull model, two data sets from the sports and health sciences are considered. The first data set represents the time-to-even data collected from different football matches during the period 1964-2018. Whereas, the second data set is taken from the health sector, representing the survival times of the COVID-19 infected patients. Based on some well-known statistical tests, it is observed that the NovEP-Weibull model is a very competitive dis-tribution for modeling the data sets in the sports and health sectors. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).