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Exploring the acceptance of <scp>COVID</scp>‐19 vaccine among healthcare workers and general population using health belief model
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Abstract<sec><title>Rationale, aims and objectives

Little is known about hesitancy to receive the COVID‐19 vaccines. The objectives of this study were (1) to assess the perceptions of healthcare workers (HCWs) and the general population regarding the COVID‐19 vaccines, (2) to evaluate factors influencing the acceptance of vaccination using the health belief model (HBM), and (3) to qualitatively explore the suggested intervention strategies to promote the vaccination.

Methods

This was a cross‐sectional study based on electronic survey data that was collected in Iraq during December first‐19th, 2020. The electronic survey was designed using Qualtrics. HBM was followed to develop the survey items. A regression analysis was used to identify factors influencing people accepting vaccination. Thematic analysis for participant comments to an open‐ended question.

Results

A total of 1680 completed surveys were received. The mean age of 31.2 ± 9.9 years, 53.0% were female and 47.0% were male. The largest group was HCWs (45.7%), followed by general population (37.5%) and health college students (16.8%). Our findings expressed some hesitancy to receive the COVID‐19 vaccine with the acceptance rate of 61.7%. The HCWs perceived significantly higher susceptibility and severity of the COVID‐19 infection compared to the general population. The HCWs were significantly more likely than the general population to receive COVID‐19 vaccine. Concerns with proper storage was the biggest barrier to vaccine receipt. The regression analysis indicated eight factors that were significantly associated with the willingness to receive COVID‐19 vaccine: Preventive measures, perceived benefit, perceived barriers, cue to action, subjective norm, supportive of vaccination in general and having received a flu vaccine before.

Conclusions

Awareness campaign can focus on enhancing the vaccine perceived benefit, debunking misconceptions, and increasing the disease perceived severity. Additionally, the public health leaders need to minimize the perceived barriers by providing the vaccines and appeasing people concerns about their storage, effectiveness, and adverse events.

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Tue Jan 01 2019
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Mon Jan 16 2023
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Evaluation of Job Satisfaction among Nurses Working at Primary Health Care Centers in Samawa City
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Objective(s): This study aimed to evaluate job satisfaction among nurses working at primary health care centers        in Samawa City.

Methodology: A Descriptive evolutional study has been carried out during the period from 1 February 2022 to 1 June 2022. A nonprobability (convenience) sample of (200) nurse were selected from different educational level. A questionnaire format is developed for the purpose of fulfilling the objectives of the study. Content validity of the questionnaire and reliability has been determined. Data are analyzed using IBM SPSS version 19 software (2010).

Results: The findings indicate that 52% of nurses are showing high level of job satisfa

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Publication Date
Fri Jul 24 2020
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Indian Journal Of Forensic Medicine &amp; Toxicology
The Incidence of Hepatitis C Virus Infections among People Screened in Governmental Health Care Facilities in 2018 in Iraq
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The Incidence of Hepatitis C Virus Infections among People Screened in Governmental Health Care Facilities in 2018 in Iraq
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Using the Input - Output Model in building the economic plan using the computer
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The origin of this technique lies in the analysis of François Kenai (1694-1774), the leader of the School of Naturalists, presented in Tableau Economique. This method was developed by Karl Marx in his analysis of the Departmental Relationships and the nature of these relations in the models of " "He said. The current picture of this type of economic analysis is credited to the Russian economist Vasily Leontif. This analytical model is commonly used in developing economic plans in developing countries (p. 1, p. 86). There are several types of input and output models, such as static model, mobile model, regional models, and so on. However, this research will be confined to the open-ended model, which found areas in practical application.

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Publication Date
Thu Oct 31 2019
Journal Name
Journal Of Engineering And Applied Sciences
Comparison of Estimate Methods of Multiple Linear Regression Model with Auto-Correlated Errors when the Error Distributed with General Logistic
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In this research, we studied the multiple linear regression models for two variables in the presence of the autocorrelation problem for the error term observations and when the error is distributed with general logistic distribution. The auto regression model is involved in the studying and analyzing of the relationship between the variables, and through this relationship, the forecasting is completed with the variables as values. A simulation technique is used for comparison methods depending

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Comparison of Estimate Methods of Multiple Linear Regression Model with Auto-Correlated Errors when the Error Distributed with General Logistic
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In this research, we studied the multiple linear regression models for two variables in the presence of the autocorrelation problem for the error term observations and when the error is distributed with general logistic distribution. The auto regression model is involved in the studying and analyzing of the relationship between the variables, and through this relationship, the forecasting is completed with the variables as values. A simulation technique is used for comparison methods depending on the mean square error criteria in where the estimation methods that were used are (Generalized Least Squares, M Robust, and Laplace), and for different sizes of samples (20, 40, 60, 80, 100, 120). The M robust method is demonstrated the best metho

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Tue Mar 30 2021
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Comparison of Some Methods for Estimating Parameters of General Linear Model in Presence of Heteroscedastic Problem and High Leverage Points
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Exploring the modulation of MLH1 and MSH2 gene expression in hesperetin-treated breast cancer cells (BT-474)
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Exploring the Challenges of Diagnosing Thyroid Disease with Imbalanced Data and Machine Learning: A Systematic Literature Review
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Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise

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