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• W3150895429 abstract "Background: The aim of this study was to analyze the public demand for information about the job of dental hygienists by mining text data collected from the online Q & A section on an Internet portal site.Methods: Text data were collected from inquiries that were posted on the Naver Q & A section from January 2003 to July 2020 using “dental hygienist job recognition,” “role recognition,” “medical assistance,” and “scaling” as search keywords. Text mining techniques were used to identify significant Korean words and their frequency of occurrence. In addition, the association between words was analyzed.Results: A total of 10,753 Korean words related to the job of dental hygienists were extracted from the text data. “Chi-lyo (treatment),” “chigwa (dental clinic),” “ske-illing (scaling),” “itmom (gum),” and “chia (tooth)” were the five most frequently used words. The words were classified into the following areas of job of the dental hygienist: periodontal disease treatment and prevention, medical assistance, patient care and consultation, and others. Among these areas, the number of words related to medical assistance was the largest, with sixty-six association rules found between the words, and “chi-lyo,” “chigwa,” and “ske-illing” as core words.Conclusion: The public demand for information about the job of dental hygienists was mainly related to “chi-lyo,” “chigwa,” and “ske-illing” as core words, demonstrating that scaling is recognized by the public as the job of a dental hygienist. However, the high demand for information related to treatment and medical assistance in the context of dental hygienists indicates that the job of dental hygienists is recognized by the public as being more focused on medical assistance than preventive dental care that are provided with job autonomy." @default.
• W3150895429 created "2021-04-13" @default.
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• W3150895429 date "2021-03-31" @default.
• W3150895429 modified "2023-10-03" @default.
• W3150895429 title "Analysis of Dental Hygienist Job Recognition Using Text Mining" @default.
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• W3150895429 doi "https://doi.org/10.17135/jdhs.2021.21.1.70" @default.
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