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Post COVID-19 Effect on Medical Staff and Doctors' Productivity Analysed by Machine Learning
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The COVID-19 pandemic has profoundly affected the healthcare sector and the productivity of medical staff and doctors. This study employs machine learning to analyze the post-COVID-19 impact on the productivity of medical staff and doctors across various specialties. A cross-sectional study was conducted on 960 participants from different specialties between June 1, 2022, and April 5, 2023. The study collected demographic data, including age, gender, and socioeconomic status, as well as information on participants' sleeping habits and any COVID-19 complications they experienced. The findings indicate a significant decline in the productivity of medical staff and doctors, with an average reduction of 23% during the post-COVID-19 period. These results reflect the overall impact observed following the entire course of the COVID-19 pandemic and are not specific to a particular wave. The analysis revealed that older participants experienced a more pronounced decline in productivity, with a mean decrease of 35% compared to younger participants. Female participants, on average, had a 28% decrease in productivity compared to their male counterparts. Moreover, individuals with lower socioeconomic status exhibited a substantial decline in productivity, experiencing an average decrease of 40% compared to those with higher socioeconomic status. Similarly, participants who slept for fewer hours per night had a significant decline in productivity, with an average decrease of 33% compared to those who had sufficient sleep. The machine learning analysis identified age, specialty, COVID-19 complications, socioeconomic status, and sleeping time as crucial predictors of productivity score. The study highlights the significant impact of post-COVID-19 on the productivity of medical staff and doctors in Iraq. The findings can aid healthcare organizations in devising strategies to mitigate the negative consequences of COVID-19 on medical staff and doctors' productivity.

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Publication Date
Sun Dec 31 2023
Journal Name
Lark
Digital and pedagogical practices of French as a foreign language in a post-COVID-19 context (The case of the University of Baghdad)
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Publication Date
Wed Apr 01 2020
Journal Name
Al-rafidain Journal For Sport Sciences
The Effectiveness of UsingflippedclassroombyQuick Response Codes In Learning Someofskills In Artistic Gymnastics for men
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The study aimed to prepare quick response codes to learn some of the technical skills of the second graders in the Faculty of Physical Education and Sports Sciences. The experimental method was used in the design of the experimental and control experimental and control groups. The research sample was represented by second-graders in the College of Physical Education and Sports Sciences / University of Baghdad, and by lot, the second division (a) was chosen to represent the experimental group that applied the inverse method using the QR code, and the second division (g) to represent the control group and applied the traditional method. (10) Students per group. After the tribal tests, his main experiment was carried out for 10 weeks with one

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Publication Date
Sat Sep 12 2020
Journal Name
Al-kindy College Medical Journal
Antenatal, Intrapartum, and Postnatal Maternal health Care during COVID-19 Pandemic
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There is limited data and evidence about the effects of COVID-19 on Maternal health, especially when new information is emerging daily, through pregnancy, child birth and post natal period, women are vulnerable to have the infection, this article, aimed to show the suitable measures that should be applied for women at reproductive age who are suspected /confirmed with COVID -19 infection,

During pregnancy it is advisable to continue the antenatal care schedule, although reducing face to face visit is recommended (unless the pregnant condition required that ),and prioritize ANC at health facilities for high-risk pregnancy and during second half of pregnancy with adequate infection prevention control  measures.

Regardi

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Publication Date
Mon Apr 04 2022
Journal Name
Journal Of Educational And Psychological Researches
The Degree of Implementation of the Coronavirus Prevention Standards (Covid-19) In the Kingdom Of Saudi Arabia (A COMPARATIVE STUDY BETWEEN FAMILIES OF PEOPLE WITH INTELLECTUAL DISABILITIES AND FAMILIES OF ORDINARY PEOPLE)
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The study aims to identify the degree of implementation of the coronavirus prevention standards (covid-19) in the kingdom of Saudi Arabia and compare it with the families of intellectual disabilities. The study population consisted of all families residing in the Kingdom of Saudi Arabia. To achieve the objectives of the research, the analytical descriptive approach was employed. The study sample consisted of (372) families, among them (84) families with intellectual disabilities, and (288) families without intellectual disabilities. They were chosen from the Saudi community according to what is available for collection in a simple random way, using the standard criteria for the prevention of coronavirus (Covid- 19) Prepared by the resear

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Publication Date
Sun Jan 01 2023
Journal Name
Revista Iberoamericana De Psicología Del Ejercicio Y El Deporte
THE IMPACT OF COVID-19 ON FOOTBALL CLUB STOCK INTEGRATION AND PORTFOLIO DIVERSIFICATION
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Publication Date
Thu Oct 27 2022
Journal Name
2022 International Conference On Engineering And Emerging Technologies (iceet)
Telemedicine Framework in COVID-19 Pandemic
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Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Intelligent Systems
A study on predicting crime rates through machine learning and data mining using text
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Abstract<p>Crime is a threat to any nation’s security administration and jurisdiction. Therefore, crime analysis becomes increasingly important because it assigns the time and place based on the collected spatial and temporal data. However, old techniques, such as paperwork, investigative judges, and statistical analysis, are not efficient enough to predict the accurate time and location where the crime had taken place. But when machine learning and data mining methods were deployed in crime analysis, crime analysis and predication accuracy increased dramatically. In this study, various types of criminal analysis and prediction using several machine learning and data mining techniques, based o</p> ... Show More
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Publication Date
Fri Apr 23 2021
Journal Name
Al-nahrain Journal Of Science
A Clinical-Statistical Study on COVID-19 Cases in Iraq: A Case Study
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Background: COVID-19 is a disease that started in Wuhan/China in late 2019 and continued through 2020 worldwide. Scientists worldwide continue to research to find vaccines, treatments, and medication for this disease. Studies also conenue to find the pathogenicity and epidemiology mechanisms. Materials and Methods: In this work, we analyzed cases obtained from Alshifaa center in Baghdad/Iraq for 23/2/2020-31/5/2020 with total instances of 797, positive cases of 393, and death cases of 30. Results: Results showed that the highest infection cases were among people aged between 41-45. Also, it was found that males' number of cases was more than females. In contrast, death cases were significantly higher in males than females. It was not

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Publication Date
Thu Mar 12 2020
Journal Name
Al-khwarizmi Engineering Journal
Improving Reverse Engineering Processes by using Articulated Arm Coordinate Measuring Machine
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The invention relates to a coordinate measuring machine (CMM) for determining a measuring position of a probe. The AACMM isdepends on the robotkinematics (forward and reverse) in their measurementprinciple, i.e., using the AACMM links and joint angles todetermine the exact workspace or part coordinates. Hence, themeasurements are obtained using an AACMM will be extremely accurate and precise since that ismerely dependent on rigid structural parameters and the only source of measurement error is due to human operators. In this paper, a new AACMM design was proposed. The new AACMM design addresses common issues such as solving the complex kinematics, overcoming the workspace limitation, avoiding singularity, and eliminating the effects of

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Publication Date
Mon Jan 01 2024
Journal Name
Bio Web Of Conferences
An overview of machine learning classification techniques
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Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed

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