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Title: | The barriers and enablers to downloading the COVIDSafe app – a topic modelling analysis |
Authors: | Smoll, Nicolas R. Walker, Jacina Khandaker, Gulam |
Keywords: | COVIDSafe contact tracing machine learning natural language processing survey |
Issue Date: | Aug-2021 |
Publisher: | Australian and New Zealand Journal of Public Health |
Series/Report no.: | Internet Use;344-347 |
Abstract: | Objective: We report a survey in regional Queensland to understand the reasons for suboptimal uptake of the COVIDSafe app. Methods: A short five-minute electronic survey disseminated to healthcare professionals, mining groups and school communities in the Central Queensland region. Free text responses and their topics were modelled using natural language processing and a latent Dirichlet model. Results: We received a total of 723 responses; of these, 69% had downloaded the app and 31% had not. The respondents’ reasons for not downloading the app were grouped under four topics: lack of perceived risk of COVID-19/lack of perceived need and privacy issues; phonerelated issues; tracking and misuse of data; and trust, security and credibility. Among the 472 people who downloaded the app and provided text amenable to text mining, the two topics most commonly listed were: to assist with contact tracing; and to return to normal. Conclusions: This survey of a regional population found that lack of perceived need, concerns around privacy and technical difficulties were the major barriers to users downloading the application. Implications for public health: Health promotion campaigns aimed at increasing the uptake of the COVIDSafe app should focus on promoting how the app will assist with contact tracing to help return to ‘normal’. Additionally, health promotors should address the app’s impacts on privacy, people’s lack of perceived need for the app and technical barriers. |
URI: | http://localhost:8080/xmlui/handle/123456789/4462 |
ISSN: | 1753-6405.13119 |
Appears in Collections: | VOL 45 NO 4 |
Files in This Item:
File | Description | Size | Format | |
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344-347.pdf | 369.41 kB | Adobe PDF | View/Open |
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