Aim: The aim of this study was to assess the level of knowledge, awareness and attitude about research ethics and research ethics committees (RECs) among dental faculties.
Materials And Methods: This cross-sectional questionnaire study incorporated a self administered questionnaire. Descriptive, Chi-square and logistic regression analysis was used to analyze the data.
Results: The overall response rate to the study was 85%. More than half were familiar with research ethics, principles and functions of the research ethics committee. Though there were some faculties whose attitude regarding research ethics principle was not optimal.
Conclusion: The present study revealed a broad acceptance among the faculties acceptance toward RECs and training in research ethics, though there existed certain gap in knowledge about research among the faculties.
Clinical Significance: This will help us to understand the knowledge, awareness and attitudes of dental faculties toward research ethics, which will help the institutional officials to develop better educational programs in the field of research ethics in order to help them conduct better research henceforth.
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http://dx.doi.org/10.5005/jp-journals-10024-1587 | DOI Listing |
for the opportunity to provide a commentary on the article, How Whiteness Shapes Nursing in Canada - What Does the Literature Say? A Rapid Review (Walker et al. 2025). I want to begin by congratulating the authors on completing a rapid review on a very important topic.
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Background: Breastfeeding in Syria is a common practice supported by social norms, family traditions, and cultural values. In Hungary, recent statistics show that exclusive breastfeeding is significantly lower than the recommendation of the World Health Organization. Understanding the perspectives of educated young ladies is crucial for discovering the difficulties of breastfeeding practices within Syrian-Hungarian societies.
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Domain-generalizable re-identification (DG Re-ID) aims to train a model on one or more source domains and evaluate its performance on unseen target domains, a task that has attracted growing attention due to its practical relevance. While numerous methods have been proposed, most rely on discriminative or contrastive learning frameworks to learn generalizable feature representations. However, these approaches often fail to mitigate shortcut learning, leading to suboptimal performance.
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