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Oral health awareness, attitude towards dental treatment, fear of infection and economic impact during COVID‐19 pandemic in the Middle East
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Abstract<sec><title>Objectives

To assess the impact of COVID‐19 on oral hygiene (OH) awareness, attitude towards dental treatment, fear of infection and economic impact in the Middle East.

Methods

This survey was performed by online distribution of questionnaires in three countries in the Middle East (Jordan, Iraq and Egypt). The questionnaire consisted of five sections: the first section was aimed at collecting demographic data and the rest sections used to assess OH awareness, attitude towards dental treatment, degree of fear and economic impact of COVID‐19. The answers were either multiple choice, closed‐end (Yes or No) or 5‐point Likert scale for the last four sections.

Results

A total of 3782 respondents completed the questionnaire. Overall, OH awareness was low (mean ±SD; 1.84 ± 1.03), while attitude towards dental treatment (5.04 ± 1.68) and economic impact (11.29 ± 4.37) were moderately affected. In addition, fear of infection showed moderately high level (14.04 ± 4.11). All subgroups of the demographic variables showed significant differences among them in relation to OH awareness and attitude towards dental treatment. The regression model suggested country, gender, age, education level, income level and fear as predictors for OH awareness (R2 0.036) and attitude towards dental care (R2 0.141).

Conclusions

The pandemic negatively impacted the level of OH awareness. However, the attitude towards dental treatment was moderately affected. Increasing public fear and compromised economy influenced OH awareness and the attitude towards dental treatment.

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Tue Jun 30 2020
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Sun Feb 25 2024
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The Effect Of Optimizers On The Generalizability Additive Neural Attention For Customer Support Twitter Dataset In Chatbot Application
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When optimizing the performance of neural network-based chatbots, determining the optimizer is one of the most important aspects. Optimizers primarily control the adjustment of model parameters such as weight and bias to minimize a loss function during training. Adaptive optimizers such as ADAM have become a standard choice and are widely used for their invariant parameter updates' magnitudes concerning gradient scale variations, but often pose generalization problems. Alternatively, Stochastic Gradient Descent (SGD) with Momentum and the extension of ADAM, the ADAMW, offers several advantages. This study aims to compare and examine the effects of these optimizers on the chatbot CST dataset. The effectiveness of each optimizer is evaluat

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Tue Jan 11 2022
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Sat Feb 14 2026
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Mon Dec 30 2013
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