Development of a culturally and linguistically sensitive virtual reality educational platform to improve vaccine acceptance within a refugee population: the SHIFA community engagement-public health innovation programme.

OBJECTIVES: To combat misinformation, engender trust and increase health literacy, we developed a culturally and linguistically appropriate virtual reality (VR) vaccination education platform using community-engaged approaches within a Somali refugee community. DESIGN: Community-based participatory research (CBPR) methods including focus group discussions, interviews, and surveys were conducted with Somali community members and expert advisors to design the educational content. Co-design approaches with community input were employed in a phased approach to develop the VR storyline. PARTICIPANTS: 60 adult Somali refugees and seven expert advisors who specialise in healthcare, autism research, technology development and community engagement. SETTING: Somali refugees participated at the offices of a community-based organisation, Somali Family Service, in San Diego, California and online. Expert advisors responded to surveys virtually. RESULTS: We find that a CBPR approach can be effectively used for the co-design of a VR educational programme. Additionally, cultural and linguistic sensitivities can be incorporated within a VR educational programme and are essential factors for effective community engagement. Finally, effective VR utilisation requires flexibility so that it can be used among community members with varying levels of health and technology literacy. CONCLUSION: We describe using community co-design to create a culturally and linguistically sensitive VR experience promoting vaccination within a refugee community. Our approach to VR development incorporated community members at each step of the process. Our methodology is potentially applicable to other populations where cultural sensitivities and language are common health education barriers.

Development of real-time assays for impedance-based detection of microbial double-stranded DNA targets: optimization and data analysis.

A real-time, label free assay was developed for microbial detection, utilizing double-stranded DNA targets and employing the next generation of an impedimetric sensor array platform designed by Sharp Laboratories of America (SLA). Real-time curves of the impedimetric signal response were obtained at fixed frequency and voltage for target binding to oligonucleotide probes attached to the sensor array surface. Kinetic parameters of these curves were analyzed by the integrated data analysis package for signal quantification. Non-specific binding presented a major challenge for assay development, and required assay optimization. For this, differences were maximized between binding curve kinetic parameters for probes binding to complementary targets versus non-target controls. Variables manipulated for assay optimization included target concentration, hybridization temperature, buffer concentration, and the use of surfactants. Our results showed that (i) different target-probe combinations required optimization of specific sets of variables; (ii) for each assay condition, the optimum range was relatively narrow, and had to be determined empirically; and (iii) outside of the optimum range, the assay could not distinguish between specific and non-specific binding. For each target-probe combination evaluated, conditions resulting in good separation between specific and non-specific binding signals were established, generating high confidence in the SLA impedimetric dsDNA assay results.