The development of effective interventions for COVID-19 vaccination has proven challenging given the unique and evolving determinants of that behavior. A tailored intervention to drive vaccination uptake through machine learning-enabled personalization of behavior change messages unexpectedly yielded a high volume of real-time short message service (SMS) feedback from recipients. A qualitative analysis of those replies contributes to a better understanding of the barriers to COVID-19 vaccination and demographic variations in determinants, supporting design improvements for vaccination interventions. Objective: The purpose of this study was to examine unsolicited replies to a text message intervention for COVID-19 vaccination to understand the types of barriers experienced and any relationships between recipient demographics, intervention content, and reply type. Method: We categorized SMS replies into 22 overall themes. Interrater agreement was very good (all κpooled . 0.62). Chi-square analyses were used to understand demographic variations in reply types and which messaging types were most related to reply types. Results: In total, 10,948 people receiving intervention text messages sent 17,090 replies. Most frequent reply types were “already vaccinated” (31.1%), attempts to unsubscribe (25.4%), and “will not get vaccinated” (12.7%). Within “already vaccinated” and “will not get vaccinated” replies, significant differences were observed in the demographics of those replying against expected base rates, all p . .001. Of those stating they would not vaccinate, 34% of the replies involved mis-/disinformation, suggesting that a determinant of vaccination involves nonvalidated COVID-19 beliefs. Conclusions: Insights from unsolicited replies can enhance our ability to identify appropriate intervention techniques to influence COVID-19 vaccination behaviors.

The higher the sensitivity of data breached, the more significant was the impact on stock prices

Social engineering is free for hackers but fraught with danger for regular users

In March 2020, more than one-third of Dark web websites data was stolen

Context analysis helps you to understand the elements of an environment and a group of potential users so that you can design a better technology project. It should involve key stakeholders, including implementing partners, donors, local and national authorities, and community members. We suggest five key lines of inquiry that context analyses should consider: People: Levels of education and literacy, information habits and needs, access to disposable income for equipment, electrical power to charge devices, and airtime and data to run them, and network access; Community: How membership of specific groups may affect access to technology and communications habits. For example, a nomadic clan may have attributable characteristics shared by its members, and variations in levels of access and freedom within the clan differentiated by gender and age. Market environment: An understanding of the key players, legal and regulatory issues, the mobile market, including both cost and distribution of agent networks, and the infrastructure, including commercial mobile infrastructure such as the availability of short-codes and APIs are all critical to making good design decisions. Political environment: understanding governance and control of, and access to, communications infrastructure by government and other actors Implementing organization: Many interventions have failed because staff were not able to maintain technology, because power or access to internet were not strong enough, because staff capacity was low or went away, or because the intervention was not supported by a broader culture of innovation and adaptive learning.

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CERN wants to borrow your computer to run simulations with MalariaControl.net - from CThings.com

Using Google Trends to identify patterns in the conversation about your product

Article on how desktop demographic software can be used in the social marketing of drug abuse prevention programs from the Indiana Prevention Resource Center.

Insight into how computing products — from websites to mobile phone software — can be designed to change what people believe and what they do.