Online Health Information Seeking by Parents for Their Children: Systematic Review and Agenda for Further Research

Kubb, Christian; Foran, Heather M.

doi:10.2196/19985

Cited by 124 publications

(125 citation statements)

References 69 publications

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“…The features of this study’s results are comprehensive and not limited to specific countries and specific topics or issue-based research. Other HISB-related review studies reported specific data such as age, college enrollment, adulthood, needs, and disease, including adolescent disease [ 82 - 84 ]. However, in this study, the results of the analysis were based on a tool for surveying healthy adults, who account for the highest proportion of the population density.…”

Section: Discussionmentioning

confidence: 99%

Characteristics of the Measurement Tools for Assessing Health Information–Seeking Behaviors in Nationally Representative Surveys: Systematic Review

Choi¹,

Jeong²

2021

J Med Internet Res

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Background The coronavirus pandemic (COVID-19) has also emerged as an infodemic, thereby worsening the harm of the pandemic. This situation has highlighted the need for a deeply rooted understanding of the health information–seeking behaviors (HISBs) of people. Objective The aim of this paper was to review and provide insight regarding methodologies and the construct of content in HISB surveys by answering the following research question: what are the characteristics of the measurement tools for assessing HISBs in nationally representative surveys around the world? Methods The Preferred Reporting Items for Systematic Reviews and Meta-Analyses was used as the framework for this study. A data search was performed through 5 international and 2 Korean databases covering the years between 2008 and 2020. Initially, studies performed among nationally representative samples were included to discover HISB survey instruments. The methodologies of the studies using HISB surveys were analyzed. For content analysis, 2 researchers reached a consensus through discussion by scrutinizing the contents of each survey questionnaire. Results A total of 13 survey tools from 8 countries were identified after a review of 2333 records from the search results. Five survey tools (Health Information National Trends Survey, Health Tracking Survey, Annenberg National Health Communication Survey, National Health Interview Survey, and Health Tracking Household Survey) from the United States, 2 instruments from Germany, and 1 tool from each of the countries of the European Union, France, Israel, Poland, South Korea, and Taiwan were identified. Telephone or web-based surveys were commonly used targeting the adult population (≥15 years of age). From the content analysis, the domains of the survey items were categorized as follows: information (information about health and patient medical records), channel (offline and online), and health (overall health, lifestyle, and cancer). All categories encompassed behavioral and attitude dimensions. A theoretical framework, that is, an information-channel-health structure for HISBs was proposed. Conclusions The results of our study can contribute to the development and implementation of the survey tools for HISB with integrated questionnaire items. This will help in understanding HISB trends in national health care.

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Section: Discussionmentioning

confidence: 99%

Characteristics of the Measurement Tools for Assessing Health Information–Seeking Behaviors in Nationally Representative Surveys: Systematic Review

Choi¹,

Jeong²

2021

J Med Internet Res

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“…Web-based health education and promotion has become increasingly popular among all age groups [1]. Although existing research on web-based health educational materials has focused on adults or general readers, there is an increasing body of research on the assessment and evaluation of web-based educational resources on children's health [2,3]. Clinical and academic research shows that effective writing styles can have an impact on the understanding and reception of medical and health educational resources for different reader groups [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Predicting Writing Styles of Web-Based Materials for Children’s Health Education Using the Selection of Semantic Features: Machine Learning Approach

Xie¹,

Ji²,

Liu³

et al. 2021

JMIR Med Inform

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Background Medical writing styles can have an impact on the understandability of health educational resources. Amid current web-based health information research, there is a dearth of research-based evidence that demonstrates what constitutes the best practice of the development of web-based health resources on children’s health promotion and education. Objective Using authoritative and highly influential web-based children’s health educational resources from the Nemours Foundation, the largest not-for-profit organization promoting children’s health and well-being, we aimed to develop machine learning algorithms to discriminate and predict the writing styles of health educational resources on children versus adult health promotion using a variety of health educational resources aimed at the general public. Methods The selection of natural language features as predicator variables of algorithms went through initial automatic feature selection using ridge classifier, support vector machine, extreme gradient boost tree, and recursive feature elimination followed by revision by education experts. We compared algorithms using the automatically selected (n=19) and linguistically enhanced (n=20) feature sets, using the initial feature set (n=115) as the baseline. Results Using five-fold cross-validation, compared with the baseline (115 features), the Gaussian Naive Bayes model (20 features) achieved statistically higher mean sensitivity (P=.02; 95% CI −0.016 to 0.1929), mean specificity (P=.02; 95% CI −0.016 to 0.199), mean area under the receiver operating characteristic curve (P=.02; 95% CI −0.007 to 0.140), and mean macro F1 (P=.006; 95% CI 0.016-0.167). The statistically improved performance of the final model (20 features) is in contrast to the statistically insignificant changes between the original feature set (n=115) and the automatically selected features (n=19): mean sensitivity (P=.13; 95% CI −0.1699 to 0.0681), mean specificity (P=.10; 95% CI −0.1389 to 0.4017), mean area under the receiver operating characteristic curve (P=.008; 95% CI 0.0059-0.1126), and mean macro F1 (P=.98; 95% CI −0.0555 to 0.0548). This demonstrates the importance and effectiveness of combining automatic feature selection and expert-based linguistic revision to develop the most effective machine learning algorithms from high-dimensional data sets. Conclusions We developed new evaluation tools for the discrimination and prediction of writing styles of web-based health resources for children’s health education and promotion among parents and caregivers of children. User-adaptive automatic assessment of web-based health content holds great promise for distant and remote health education among young readers. Our study leveraged the precision and adaptability of machine learning algorithms and insights from health linguistics to help advance this significant yet understudied area of research.

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“…Given the volume of information behavior research, conducting an overall literature review on this topic is challenging, and “reviews of the information behavior literature grew more specialized” (Case, 2006, p. 294). Literature reviews tend to focus on the information behavior of one group, such as youth (Agosto, 2019; Shenton, 2018), farmers (Phiri, Chipeta, & Chawinga, 2019), professors (Nwone & Mutula, 2019), consumers (Pian, Song, & Zhang, 2020; Zhao & Zhang, 2017), students (Mutula & Majinge, 2016; Oliveira & Greenidge, 2020; Spezi, 2016), parents (Kubb & Foran, 2020), medical or public health workers (Barr‐Walker, 2017; González‐Teruel, Campos‐Peláez, & Fortea‐Cabo, 2020; Prakasan, 2013), or people with impairments (Berget & MacFarlane, 2020). Other reviews tended to concentrate on studying one type of information, like health information (Chi, Pian, & Zhang, 2020; Kubb & Foran, 2020; Zhao & Zhang, 2017), musical information (Lavranos et al, 2016), or visual information (Albertson, 2015).…”

Section: Resultsmentioning

confidence: 99%

What Does the Literature Tell Us? Reviewing Literature Reviews on Information Behavior

Zhang

Bartlett

2021

Proceedings of the Association for Information Science and Tech

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Many reviews on information behavior have been published to date, however little research has been done to understand these reviews. To fill this gap, this poster reviewed 96 literature reviews in this field. The preliminary analysis uncovers several characteristics of literature reviews on information behavior: (1) these reviews have discussed two transformations in the history of information behavior; (2) current literature reviews focus on specialized areas of information behavior; (3) literature reviews have highlighted different issues in information behavior research, such as the quality of information and the practical contributions of research; (4) literature reviews keep summarizing and developing theories and models of information behavior. This preliminary analysis provides an overview of 30 years of literature reviews, showing what existing literature reviews have done. It identifies trends in information behavior research and may inspire what kinds of literature reviews are needed in the future.

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Online Health Information Seeking by Parents for Their Children: Systematic Review and Agenda for Further Research

Cited by 124 publications

References 69 publications

Characteristics of the Measurement Tools for Assessing Health Information–Seeking Behaviors in Nationally Representative Surveys: Systematic Review

Characteristics of the Measurement Tools for Assessing Health Information–Seeking Behaviors in Nationally Representative Surveys: Systematic Review

Predicting Writing Styles of Web-Based Materials for Children’s Health Education Using the Selection of Semantic Features: Machine Learning Approach

What Does the Literature Tell Us? Reviewing Literature Reviews on Information Behavior

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