Pretesting of Infographic as a Communication Tool on Pregnant Women During Covid-19 Period

This study was to analyze the responses of informants about Instagram infographics on Covid-19 prevention for pregnant women.

This was a qualitative study that used Rapid Assessment Procedure (RAP) and used pretesting communication theory. The informant selection technique was purposive sampling that consist of three pregnant women as main informants, a midwifery lecturer and a visual graphic designer as key informants. One-to-one pretesting communication procedure was selected because the research was conducted at the beginning of the Covid-19 pandemic, so it had a difficulty in recruiting informants. The interview guideline was conducted by the research team and was examined in a field trial. Data collection was by semi-structured interview using voice call WhatsApp application. Data were analysed using thematic analysis.

In attraction aspect, this was considered quite interesting by the informants. In comprehension aspect, the messages were easily understood because of using brief, concise, and simple sentences. Furthermore, the messages were supported by images and comprehensive. In acceptance aspect, all the informants' opinions were identified that the messages of this infographic did not have a conflict with the existing norms. In self-involvement aspect, this infographic was in accordance with the current condition of the informants. In persuasion aspect, it had a good persuasive value as the informants were willing to share the infographic with others.

The infographic still needed improvements from the attraction aspect such as consider using contrasting colour between the background and text, equalize the font size and change icons to become related to the text. As from the comprehension aspect consider using terms that are more popular in the community. There were no need improvements from acceptance, self-involvement, and persuasion aspects. However, evidence-based research is still needed on how this infographic is developed and implemented to optimize transfer of knowledge.