College student food insecurity (FI) is a public health concern. Programming and policies to support students have expanded but utilisation is often limited. The aim of this study was to summarise the barriers to accessing college FI programming guided by the social ecological model (SEM) framework. A scoping review of peer-reviewed literature included an electronic search conducted in MEDLINE, ERIC, and PubMed databases, with a secondary search in Google Scholar. Of the 138 articles identified, 18 articles met eligibility criteria and were included. Articles primarily encompassed (17/18) level barriers, followed by (15/18), (15/18), (9/18), and (6/18) levels. barriers included seven themes: . Four relationship barriers were identified: , , , and . Ten barrier themes comprised the organisational level: , , , , , , , , and . Two barrier themes were identified at the level, and , while one barrier theme, , encompassed the level. Higher education stakeholders should seek to overcome these barriers to the use of food programmes as a means to address the issue of college FI. This review offers recommendations to overcome these barriers at each SEM level.
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http://dx.doi.org/10.1017/jns.2024.25 | DOI Listing |
J Eval Clin Pract
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Department of Anatomy, Medical College, Jinan University, Guangdong, China.
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Dongguan Key Laboratory of Stem Cell and Regenerative Tissue Engineering, The First Dongguan Affiliated Hospital, School of Basic Medical Sciences, Guangdong Medical University, Dongguan, 523808, China.
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Department of Gastroenterology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China.
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January 2025
Department of Prosthodontics and Dental Implantology, College of Dentistry, King Faisal University, Al-Ahsa, Saudi Arabia. Electronic address:
The integration of artificial intelligence (AI) into dental imaging has led to significant advancements, particularly in the analysis of panoramic radiographs, also known as orthopantomograms (OPGs). One emerging application of AI is in determining gender from these radiographs, a task traditionally performed by forensic experts using manual methods. This systematic review and meta-analysis aim to evaluate the accuracy of AI algorithms in gender determination using OPGs, focusing on the reliability and potential clinical and forensic applications of these technologies.
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