Developing an Indonesia's health literacy short-form survey questionnaire (HLS-EU-SQ10-IDN) using the feature selection and genetic algorithm

Rachmani, Enny; Hsu, Chien Yeh; Nurjanah, Nurjanah; Chang, Peter; Shidik, Guruh Fajar; Noersasongko, Edi; Jumanto, Jumanto; Fuad, Anis; Ningrum, Dina Nur Anggraini; Kurniadi, Arif; Lin, Ming‐Chin

doi:10.1016/j.cmpb.2019.105047

Cited by 21 publications

(19 citation statements)

References 28 publications

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“…In Eastern Europe, the scale was validated on a representative sample of the general parent population in Hungary [ 46 ] and in the adult population of Poland [ 9 ]. Even outside Europe, mostly in Asian countries, this short measure was validated and used in general populations in Israel [ 47 ], Indonesia [ 48 ] and Turkey [ 49 ] as well as in an outpatient setting in Egypt [ 19 ]. In all cases, the scale provided proof of good psychometric properties.…”

Section: Methodsmentioning

confidence: 99%

The Sociodemographic Determinants of Health Literacy in the Ethnic Hungarian Mothers of Young Children in Eastern Europe

Sántha

2021

IJERPH

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Parental health literacy is a decisive factor for child health and quality of life. Children of parents with limited health literacy are at increased risk of illness and longer recovery periods. The research at the Quality of Life Research Centre is aimed at studying the health literacy of ethnic Hungarian mothers in Eastern Europe (Hungary, Slovakia, Romania) as well as at assessing its socioeconomic and demographic antecedents. The sample size is 894 mothers. Our standardized online questionnaire includes the HLS-EU-16 and the BHLS questions, with the latter intended to screen for inadequate health literacy. Predictors of health literacy in mothers are socioeconomic status, age and partnership status. A key finding is the improvement of health literacy with age. Assessing the association of partnership status and health literacy is a novelty in this region. Our analysis reinforces the role of socioeconomic capital, widely recognized to be associated with health literacy in general and with parental health literacy in particular. Results indicate the necessity of improving caregiver health literacy with a range of health promotional activities in Eastern Europe, especially among mothers with low socioeconomic status. The hardships of young mothers and single mothers should also be considered in this respect.

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Section: Methodsmentioning

confidence: 99%

The Sociodemographic Determinants of Health Literacy in the Ethnic Hungarian Mothers of Young Children in Eastern Europe

Sántha

2021

IJERPH

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“…Hal ini dapat mempengaruhi tingkat kemampuan dalam mengakses, memahami, menilai dan mengaplikasi informasi kesehatan. Lebih lanjut (Nurjanah and Rachmani, 2015;Nutbeam, 2017;Rachmani et al, 2019) menjelaskan informasi dan pendidikan kesehatan dari penyedia layanan kesehatan sebagai media sebagai alat penyebaran informasi kesehatan menjadi akses Mengubah perilaku di masyarakat dapat dilakukan dengan literasi kesehatan (Sorensen et al, 2012;Sorensen and Brand, 2013;Oo et al, 2015;Okan et al, 2019;Shi et al, 2020), sehingga literasi kesehatan merupakan variabel penting untuk memberdayakan kesehatan pada individu khususnya dan masyarakat umumnya. Literasi kesehatan telah diakui sebagai salah satu faktor dari variabel kesehatan dan menjadi salah satu tujuan pembangunan kesehatan masyarakat (Nutbeam, 2017;Okan et al, 2019;.…”

Section: Pendahuluanunclassified

“…Tabel 1 menampilkan hasil analisis distribusi frekuensi dari pemahaman literasi kesehatan pada responden dengan menggunakan kuisioner yang telah terstandar dan dapat digunakan di Indonesia yang bersumber dari artikel Rachmani et al (2019). Sebanyak 10 pernyataan diberikan untuk mengetahui tingkat pemahaman literasi kesehatan, selanjutnya dibagi atas 4 tingkat.…”

Section: Hasilunclassified

Hubungan Demografi Dan Penerapan Protokol Covid-19 Dengan Literasi Kesehatan Di Kelurahan Silaberanti Kota Palembang

Said¹,

Ropiah

2021

PREPOTIF

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Literasi Kesehatan merupakan upaya meningkatkan kesadaran melalui kemudahan masyarakat untuk mengakses informasi tentang kesehatan. Dewasa ini penerapan protokol kesehatan belum berjalan maksimal karena kurangnya pemahaman literasi kesehatan masyarakat. Tujuan dari penelitian ini adalah untuk mengetahui hubungan antara demografi (umur, jenis kelamin, pekerjaan, pendidikan, pendapatan) dan penerapan protokol COVID-19 (3M) dengan tingkat pemahaman literasi kesehatan di Kelurahan Silaberanti Kota Palembang. Metode penelitian menggunakan penelitian kuantitatif dengan rancangan studi potong lintang dengan jumlah sampel sebanyak 100 responden. Penelitian ini dilakukan di Kelurahan Silaberanti Kota Palembang dan dilaksanakan pada bulan Februari sampai dengan April 2021. Hasil penelitian menunjukkan bahwa ada hubungan antara pekerjaan dan menjaga jarak dengan pemahaman literasi kesehatan di Kelurahan Silaberanti Kota Palembang.

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“…In this study, the feature selection task was performed using GA (Gómez & Quesada, 2017;Rachmani et al, 2019). GA is an important heuristic algorithm inspired by the natural evolution procedure.…”

Section: Genetic Algorithm-based Feature Selectionmentioning

confidence: 99%

Estimation of healthcare expenditure per capita of Turkey using artificial intelligence techniques with genetic algorithm‐based feature selection

Ceylan

Atalan

2020

Journal of Forecasting

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This study presents a comprehensive analysis of artificial intelligence (AI) techniques to predict healthcare expenditure per capita (pcHCE) in Turkey. Well-known AI techniques such as random forest (RF), artificial neural network (ANN), multiple linear regression (MLR), support vector regression (SVR), and relevance vector machine (RVM) were used to forecast pcHCE. Twenty-nine years of historical data from 1990 to 2018 were used in the training and testing phases of the models. Gross domestic product per capita, life expectancy at birth, unemployment rate, crude birth rate, and the number of physicians and hospitals were used as input variables for the analysis. A genetic algorithm-based feature selection (GAFS) method was applied to all models to select the relevant and optimal feature subset in the prediction of pcHCE. The comparative results showed that the GAFS method improved the overall performance of all base AI models. The hybrid GAFS-RF model performed best among all AI-based prediction methods, with a 99.86% correlation of determination (R 2 ) value at the testing stage.

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Developing an Indonesia's health literacy short-form survey questionnaire (HLS-EU-SQ10-IDN) using the feature selection and genetic algorithm

Cited by 21 publications

References 28 publications

The Sociodemographic Determinants of Health Literacy in the Ethnic Hungarian Mothers of Young Children in Eastern Europe

The Sociodemographic Determinants of Health Literacy in the Ethnic Hungarian Mothers of Young Children in Eastern Europe

Hubungan Demografi Dan Penerapan Protokol Covid-19 Dengan Literasi Kesehatan Di Kelurahan Silaberanti Kota Palembang

Estimation of healthcare expenditure per capita of Turkey using artificial intelligence techniques with genetic algorithm‐based feature selection

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