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Vaccine hesitancy is defined as 'delay in acceptance or refusal of vaccines, despite the availability of vaccine service' and was named one of the top ten global health threats by the World Health Organization in 2019.
Our proposed study will aim to develop and evaluate the effectiveness of a Web-based psychoeducation programme to address Covid-19 vaccine hesitancy, 'AI-driven Vaccine Communicator' (including educational materials, animations of vaccine research and development, and an MI communication skills-based AI, digital assistant).
Our goal is to standardise our intervention so that it can serve as an effective toolkit for clinicians/healthcare providers to increase Hong Kong residents' motivation to vaccinate and to ensure that the programme can be adapted to viral mutations and newly developed vaccines in the medium/long term.
Full description
This study is a two-arm, single-blind pilot randomized controlled trial, and it will be adopted to evaluate the efficacy of the 'AI-driven Vaccine Communicator' relative to being self-informed about Covid-19 vaccines.
Also, to address the widespread hesitancy among Hong Kong residents toward Covid-19 vaccines, we will develop a Web-based psychoeducation programme, 'AI-driven Vaccine Communicator,' driven by artificial intelligence (AI) techniques based on the Vaccine Hesitancy Determinants Matrix Model and MI communication skills and concepts (theoretical underpinnings). After developing and piloting this programme, we will test its effectiveness among members of the population who are hesitant about Covid-19 vaccines. The programme will then be refined further and standardised to use the most relevant information and effective communication skills to promote acceptance of Covid-19 vaccines among a wider Hong Kong population. Clinicians and healthcare providers will be able to utilise our programme as a toolkit in their usual practice when working with clients who are hesitant about Covid-19 vaccines.
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168 participants in 2 patient groups
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Yan Li, Dr; Kit Ching Lee, Miss
Data sourced from clinicaltrials.gov
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