Casting a Wide Net: HIV Drug Resistance Monitoring in Pre-Exposure Prophylaxis Seroconverters in the Global Evaluation of Microbicide Sensitivity Project

Background: Evidence of HIV drug resistance (HIVDR) in individuals using oral pre-exposure prophylaxis (PrEP) who acquire HIV is limited to clinical trials and case studies. More data are needed to understand the risk of HIVDR with oral PrEP during PrEP rollout. Mechanisms to collect these data vary, and are dependent on cost, scale of PrEP distribution, and in-country infrastructure for the identification, collection, and testing of samples from PrEP seroconverters. / Methods: The Global Evaluation of Microbicide Sensitivity (GEMS) project, in collaboration with country stakeholders, initiated HIVDR monitoring among new HIV seroconverters with prior PrEP use in Eswatini, Kenya, South Africa, and Zimbabwe. Standalone protocols were developed to assess HIVDR among a national sample of PrEP users. In addition, HIVDR testing was incorporated into existing demonstration projects for key populations. / Lessons learned: Countries are supportive of conducting a timelimited evaluation of HIVDR during the early stages of PrEP rollout. As PrEP rollout expands, the need for long-term HIVDR monitoring with PrEP will need to be balanced with maintaining national HIV drug resistance surveillance for pretreatment and acquired drug resistance. Laboratory capacity is a common obstacle to setting up a monitoring system. / Conclusions: Establishing HIV resistance monitoring within PrEP programs is feasible. Approaches to drug resistance monitoring may evolve as the PrEP programs mature and expand. The methods and implementation support offered by GEMS assisted countries in developing methods to monitor for drug resistance that best fit their PrEP program needs and resources

Encouraging Teacher-sourcing of Social Recommendations Through Participatory Gamification Design

Teachers and learners who search for learning materials in open educational resources (OER) repositories greatly benefit from feedback and reviews left by peers who have activated these resources in their class. Such feedback can also fuel social-based ranking algorithms and recommendation systems. However, while educational users appreciate the recommendations made by other teachers, they are not highly motivated to provide such feedback by themselves. This situation is common in many consumer applications that rely on users’ opinions for personalisation. A possible solution that was successfully applied in several other domains to incentivise active participation is gamification. This paper describes for the first time the application of a comprehensive cutting-edge gamification taxonomy, in a user-centred participatory-design process of an OER system for Physics, PeTeL, used throughout Israel. Physics teachers were first involved in designing gamification features based on their preferences, helping shape the gamification mechanisms likely to enhance their motivation to provide reviews. The results informed directly the implementation of two gamification elements that were implemented in the learning environment, with a second experiment evaluating their actual effect on teachers’ behaviour. After a long-term, real-life pilot of two months, teachers’ response rate was measured and compared to the prior state. The results showed a statistically significant effect, with a 4X increase in the total amount of recommendations per month, even when taking into account the ‘Covid-pandemic effect’

Recommender systems for teachers: The relation between social ties and the effectiveness of socially-based features

Open Educational Resources (OER) repositories provide teachers with a wide range of learning resources (LRs), enabling them to design various learning sequences. However, search & select in large OER repositories can be a daunting task for teachers. Incorporating peer recommendations, as is common in online marketplaces, is becoming a popular solution that seeks to exploit the wisdom of the crowd for this task. However, teachers are often reluctant to take a contributory role and provide social recommendations. In addition, little is known about the actual value of social recommendations as a search aid. In this research, we implemented a “light-weight” socially-based recommender system (RS) within a large OER repository that includes social network features. We examined two aspects of the socially-based recommendation mechanisms. First, their utility as search aids that assist teachers in searching and selecting suitable LRs, and second, their impact on teachers' incentives to share recommendations that can assist fellow teachers. To study these two aspects, we examined two science teacher communities using this repository. The results demonstrated the incentivising power of social rewards, and the value of social recommendations as means for search & select. However, we also observed a heterogeneous effect of social features on teachers' behaviour. To explore the factors that may explain these differences, we employed a mixed-method approach, combining qualitative, quantitative, and Social Network Analysis methods. Triangulation of the findings underline the relation between the strength of the social ties within the teachers’ community and the effectiveness of socially-based features