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

A scoping review into the service needs of people from culturally and linguistically diverse backgrounds living with disability to engage in meaningful occupations

INTRODUCTION

Although there is a large proportion of people from culturally and linguistically diverse backgrounds within Australia, their rate of access to disability services is disproportionately low. This review aims to understand the service needs of people from culturally and linguistically diverse backgrounds with disability to facilitate engagement in meaningful occupations.

METHODS

Arksey and O'Malley's scoping review framework was employed. Ten databases were searched for Australian studies. A deductive content analysis framework was applied in the synthesis.

RESULTS

Fourteen papers were included. Themes that emerged include language and cultural needs and considerations, which highlights the need for information sharing to take account of intergenerational, intercultural and sociolinguistic differences. It also identified the need for improved training and skills of existing interpreters. Culturally competent and responsive services was another theme identified, which emphasised the need to enhance the workforces' understanding of cultural practices. There is also a strong call for a more culturally diverse workforce to reduce the use of some interpreters and to build a more culturally competent workforce. The last theme was responsive service delivery, which requires the governance to support the development of a nurturing trusting therapeutic relationship.

CONCLUSIONS

Service providers should be trained on the inequities and intersectionality of this population. Further research is required to explore current disability policy in Australia with an intersectionality lens to ensure recommendations can be made to address barriers and ensure this population receives services in a manner that enhances their ability to engage in occupations meaningfully.

Evaluating a Public Health Information Service According to Users' Socioeconomic Position and Health Status: Protocol for a Cross-Sectional Study

BACKGROUND

The increasing use of information technology in the field of health is supposed to promote users' empowerment but can also reinforce social inequalities. Some health authorities in various countries have developed mechanisms to offer accurate and relevant information to health care system users, often through health websites. However, the evaluation of these sociotechnical tools is inadequate, particularly with respect to differences and inequalities in use by social groups.

OBJECTIVE

Our study aims to evaluate the access, understanding, appraisal, and use of the French website Santé.fr by users according to their socioeconomic position and perceived health status.

METHODS

This cross-sectional study involves the entire French population to which Santé.fr is offered. Data will be collected through mixed methods, including a web-based questionnaire for quantitative data and interviews and focus groups for qualitative data. Collected data will cover users' access, understanding, appraisal, and use of Santé.fr, as well as sociodemographic and socioeconomic characteristics, health status, and digital health literacy. A validation of the dimensions of access, understanding, appraisal, and use of Santé.fr will be conducted, followed by principal component analysis and ascendant hierarchical classification based on the 2 main components of principal component analysis to characterize homogeneous users' profiles. Regression models will be used to investigate the relationships between each dimension and socioeconomic position and health status variables. NVivo 11 software (Lumivero) will be used to categorize interviewees' comments into preidentified themes or themes emerging from the discourse and compare them with the comments of various types of interviewees to understand the factors influencing people's access, understanding, appraisal, and use of Santé.fr.

RESULTS

Recruitment is scheduled to begin in January 2024 and will conclude when the required number of participants is reached. Data collection is expected to be finalized approximately 7 months after recruitment, with the final data analysis programmed to be completed around December 2024.

CONCLUSIONS

This study would be the first in France and in Europe to evaluate a public health information service, in this case the Santé.fr website (the official website of the French Ministry of Health), according to users' socioeconomic position and health status. The study could discover issues related to inequalities in access to, and the use of, digital technologies for obtaining health information on the internet. Given that access to health information on the internet is crucial for health decision-making and empowerment, inequalities in access may have subsequent consequences on health inequalities among social categories. Therefore, it is important to ensure that all social categories have access to Santé.fr.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/51123.

National disability insurance scheme access: What evidence do you need to provide for psychosocial disability?

OBJECTIVES

The National Disability Insurance Scheme (NDIS) was introduced in 2013 and offered a new way of providing support to people with permanent and significant disabilities. Despite pilot testing, implementation of the scheme has been challenging, particularly for people with a disability arising from a mental health condition. In 2019, to address the challenge of accessing the NDIS, researchers from Flinders University worked with the National Disability Insurance Agency (NDIA) to develop a streamlined access process for psychosocial disability. The aim of this paper is to provide guidance on the evidence required to demonstrate that a person has a significant and persistent psychosocial disability to access the NDIS.

CONCLUSION

Providing evidence for a psychosocial disability requires knowledge of how to address the disability requirements. The Evidence of Psychosocial Disability (EPD) form has been designed to address these requirements and offers guidance on the evidence that should be provided. A range of resources to accompany the EPD form are freely available online. These resources address a significant knowledge gap that currently exists with the implementation of the NDIS.

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

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.

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

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.