ESTG - Mestrado em Ciência de Dados
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- Health literacy of the Leiria Polytechnic AcademiaPublication . Ascenso, Rita Margarida Teixeira; Dias, Sara Alexandra da Fonseca Marques Simões; Luís, Luís Francisco Soares; Gonçalves, Dulce Cristina dos Santos IriaLiteracy spans from economic, and financial to digital and information literacy, environmental and environmental sustainability and energy, and statistics literacy to health literacy. Health literacy (HL) has several definitions and numerous HL assessment tools. World Health Organization (WHO) has the 1998 Health Promotion Glossary and defines HL; “Health literacy implies the achievement of a level of knowledge, personal skills and confidence to take action to improve personal and community health by changing personal lifestyles and living conditions”. Several systematic reviews on HL identified tools for HL assessment, namely: Short Assessment of Health Literacy (SAHL) questionnaire and the European Health Literacy Survey with 47 questions (HLS-EU-Q47), with 16 questions (HLS-EU-Q16) , and with only 6 questions (HLS-EU-Q6). These have already been translated into Portuguese and used to assess HL since 2017. In a study published in 2022, Pedro et. al. described that almost half of higher education students in Portugal had inadequate or problematic health literacy levels, with no significant differences between first and last-year students but variations among health-related courses. Data revealed that HL tends to be adequate or excellent among those students with health-related degrees. Limited HL is confirmed at university and higher education students. Is it true in the Polytechnic of Leiria academia? This work follows the CRISP-DM methodology, used for data mining, to perform the exploratory analysis of existing data. Data came from the survey applied to the academia of the Polytechnic of Leiria. HLS-EU-Q16 was applied between 8th December 2020 and 26th March 2021 and saved. Data mining was proposed as a dissertation in the first edition of Data Science Masters. The objectives to be fulfilled by executing the work, started with a Literature review, from a perspective of business understanding and text mining was used. Dataset characteristics were deeply analysed, starting from 431 survey participants. After the whole sample analysis, data mining for students’ subset was performed. Classification analysis and predictive data analysis were performed to achieve School classification based on HL index.