Help or hinder? An assessment of the accessibility, usability, reliability and readability of disability funding website information for Australian mental health consumers

Visser, Kristin; Slattery, Maddy; Stewart, Victoria

doi:10.1111/hsc.13192

Cited by 10 publications

(14 citation statements)

References 43 publications

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“…Key assessment criteria of PEMAT include health information understandability, relevance, and actionability [8,9]. Much of the current research has focused on exploring these assessment dimensions separately using long-standing readability tools [10][11][12][13] or machine learning algorithms of natural language features [14][15][16] using features such as general medical vocabularies, consumer medical vocabulary, natural language features such as a part of speech features [17][18][19], and other metadata [20]. Furthermore, many of these data-intensive and data-driven studies did not consider insights from research fields directly relevant to health educational resource development and evaluation.…”

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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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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“…Effective multilingual communication of authoritative health information plays an important role in helping to reduce health disparities, inequalities in developed and developing countries. Currently, the evaluation of the quality of public-oriented health information focused on readability assessment [1][2][3][4][5][6][7]. In 2013, the Agency for Healthcare Research and Quality of the U.S. Department of Health and Human Services developed the Patient Education Materials Assessment Tool (PEMAT) which extended the evaluation of the quality of user-oriented health information from readability, understandability to include Int.…”

Section: Introductionmentioning

confidence: 99%

Assessing Communicative Effectiveness of Public Health Information in Chinese: Developing Automatic Decision Aids for International Health Professionals

Bodomo

Xie

et al. 2021

IJERPH

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Effective multilingual communication of authoritative health information plays an important role in helping to reduce health disparities and inequalities in developed and developing countries. Health information communication from the World Health Organization is governed by key principles including health information relevance, credibility, understandability, actionability, accessibility. Multilingual health information developed under these principles provide valuable benchmarks to assess the quality of health resources developed by local health authorities. In this paper, we developed machine learning classifiers for health professionals with or without Chinese proficiency to assess public-oriented health information in Chinese based on the definition of effective health communication by the WHO. We compared our optimized classifier (SVM_F5) with the state-of-art Chinese readability classifier (Chinese Readability Index Explorer CRIE 3.0), and classifiers adapted from established English readability formula, Gunning Fog Index, Automated Readability Index. Our optimized classifier achieved statistically significant higher area under the receiver operator curve (AUC of ROC), accuracy, sensitivity, and specificity than those of SVM using CRIE 3.0 features and SVM using linguistic features of Gunning Fog Index and Automated Readability Index (ARI). The statistically improved performance of our optimized classifier compared to that of SVM classifiers adapted from popular readability formula suggests that evaluation of health communication effectiveness as defined by the principles of the WHO is more complex than information readability assessment. Our SVM classifier validated on health information covering diverse topics (environmental health, infectious diseases, pregnancy, maternity care, non-communicable diseases, tobacco control) can aid effectively in the automatic assessment of original, translated Chinese public health information of whether they satisfy or not the current international standard of effective health communication as set by the WHO.

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“…To be effective, the design of messages and messaging (i.e., promotion material) should be culturally responsive to the diverse ways of obtaining, processing, and understanding information (Natkunam et al, 2020;Ross & Ross, 2021). Yet, large-scale evaluations of PA and health-related digital messaging have revealed materials that are largely inaccessible for diverse populations due to unsuitable required reading grade levels , non-compliance with disability access standards for digital media, including issues with font style, content layout, and lack of alternative text for images (Shaw 2017;Visser et al, 2021;Disability Rights Commission, 2004), and poor or missing representation of persons with disabilities (Bruning et al 2020;Comella et al, 2019;Mitchell et al, 2019).…”

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confidence: 99%

“…(McManus, 2022). Thus, intentional educational efforts to prepare professionals to apply techniques for health literacy promotion and recognize the value of this practice in support of health equity, is needed (May et al, 2022;Visser et al, 2021). Despite this call to action, there has been limited improvement over the past quarter century in the accessibility of health promotion materials (Cardinal & Sachs, 1992;Thomas & Cardinal, 2020).…”

mentioning

confidence: 99%

“…This study focused on the inclusivity of individuals with disabilities, while recognizing that culturally competent professionals demonstrate appreciation for diversity across gender, age, race/ethnicity, religion, ability, and other intersections of identity (AKA competency #81, Chodzko-Zajko et al, 2018). Individuals with disabilities are largely underrepresented within health-related messaging (Visser et al, 2021), including web-based sport and PA promotion (Hardin et al, 2001;Martinez-Bello, 2017) and programming (Comella et al, 2019). To effectively design disability inclusive PA materials, kinesiology professionals need to recognize this current issue of underrepresentation and its historical/cultural impact on participation and health among individuals with disabilities (AKA competency #7 & 9, Chodzko-Zajko et al, 2018).…”

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confidence: 99%

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Exploring learning outcomes among undergraduate kinesiology students in response to an inclusive physical activity promotion message assignment

Ross

Thomas

2022

Jkinwellness

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Numerous professional bodies and curricular models in kinesiology call for the development of undergraduates’ cultural competency. In short, a culturally competent professional (a) mitigates personal biases from adversely affecting others, and (b) is aware of how societal patterns marginalize people (e.g., unquestioned norms; Gill, 2007). The present study aimed to evaluate the extent to which undergraduate students demonstrated learning in response to completing an inclusive physical activity promotion message assignment, which included a focus on designing materials inclusive of people with disabilities. A scholarship of teaching and learning approach was adopted to critically evaluate and reflect on an assignment used in an undergraduate kinesiology course. This case study drew on artifacts from a convenience sample of 10 undergraduate students enrolled in an introduction to adapted physical activity course. A descriptive discourse analysis was conducted of students’ reflections about what they learned from the assignment. Student responses were appraised with Fink’s (2013) taxonomy of significant learning for evidence of learning across six domains: i.e., foundational knowledge, application, integration, human dimension, caring, and self-determination. Student responses were coded and verified. Consensus was reached on all discrepancies. Student reflections signaled learning across four domains: foundational knowledge (n = 9), application (n = 3), integration (n = 5), and human dimensions (n = 6). Developments in the caring or self-determination domains were not evident. The findings indicate potential for the assignment to enhance Kinesiology curricula. As teacher-researchers, we discuss the findings in relation to further development of the assignment to better promote cultural competency.

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Help or hinder? An assessment of the accessibility, usability, reliability and readability of disability funding website information for Australian mental health consumers

Cited by 10 publications

References 43 publications

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

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

Assessing Communicative Effectiveness of Public Health Information in Chinese: Developing Automatic Decision Aids for International Health Professionals

Exploring learning outcomes among undergraduate kinesiology students in response to an inclusive physical activity promotion message assignment

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