The results of recently completed trials in Africa of insecticide-treated bed nets (ITBN) offer new possibilities for malaria control. These experimental trials aimed for high ITBN coverage combined with high re-treatment rates. Whilst necessary to understand protective efficacy, the approaches used to deliver the intervention provide few indications of what coverage of net re-treatment would be under operational conditions. Varied delivery and financing strategies have been proposed for the sustainable delivery of ITBNs and re-treatment programmes. Following the completion of a randomized, controlled trial on the Kenyan coast, a series of suitable delivery strategies were used to continue net re-treatment in the area. The trial adopted a bi-annual, house-to-house re-treatment schedule free of charge using research project staff and resulted in over 95% coverage of nets issued to children. During the year following the trial, sentinel dipping stations were situated throughout the community and household members informed of their position and opening times. This free re-treatment service achieved between 61-67% coverage of nets used by children for three years. In 1997 a social marketing approach, that introduced cost-retrieval, was used to deliver the net re-treatment services. The immediate result of this transition was that significantly fewer of the mothers who had used the previous re-treatment services adopted this revised approach and coverage declined to 7%. The future of new delivery services and their financing are discussed in the context of their likely impact upon previously defined protective efficacy and cost-effectiveness estimates.

Social media platforms have broadened the scope of voices responding to social justice movements, significantly impacting public conversations of important social justice issues. This social network analysis examined hashtags that were invoked on Twitter in the aftermath of the Mike Brown shooting in the St. Louis suburb of Ferguson in 2014. From the millions of tweets globally, the use of specific hashtags appeared to focus the conversation on Twitter toward the personal meaning of story events and framed the shooting as something relatable to the posters’ own lives and experiences.

Our study uses computational archaeology tools to investigate how researchers in our field present interpretations of the past in patterned ways. We do so in order to illuminate assumptions, naturalised categories, and patterned interpretative moves that may direct or impact the ways we interact with our evidence and write about our research. We approach this topic through a meta-analysis, using large-scale textual data from archaeological publications, focusing on the case study of bone. Are there patterned ways that archaeologists write about artefacts like bone that are visible when analysing larger datasets? If so, what underlying ideas shape these shared discursive moves? We present the results of three analyses: textual groundwork, conducted manually by field experts, and two machine-based interactive topic modelling visualisations (pyLDAvis and a hierarchical tree based on a Model of Models). Our results indicate that there are, indeed, patterns in our writing around how artefactual and archaeological materials are discussed, many of which are overt and sensical. However, our analyses also identify patterned discourses that are less obvious, but still part of regularised discourses in written narratives surrounding bone. These include: the use of multiple conceptual positions within, rather than simply between, articles, and a lack of patterned centrality of indigenous ontologies in how our field writes about bone. This pilot approach identifies data-informed, applied tools that will aid reflexive practices in our field. These operate at a scale that impacts future scholarly interactions with both evidence and published interpretations by shifting observation and reflection from an individual or small group exercise to a larger and more systematic process.

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Background and Objectives: The medical community has been concerned about the shortage of family physicians for decades. Identification of likely family medicine (FM) student matches early in medical school is an efficient recruitment tool. The objective of this study was to analyze qualitative data from medical school applications to establish themes that differentiate future family physicians from their non-FM counterparts. Methods: We conducted a qualitative analysis of admissions essays from two groups of 2010-2019 medical school graduates: a study group of students who matched to FM (n=135) and a random sample comparison group of non-FM matches (n=136). We utilized a natural language modeling platform to recognize semantic patterns in the data. This platform generated keywords for each sample, which then guided a more traditional content analysis of the qualitative data for themes. Results: The two groups shared two themes: emotions and science/academics, but with some differences in thematic emphasis. The study group tended toward more positive emotions and the comparison group tended to utilize more specialized scientific language. The study group exhibited two unique themes: special interests in service and community/people. A secondary theme of religious faith was evident in the FM study group. The comparison group exhibited two unique themes: lab/clinical research and career aspirations. Conclusions: Aided by machine learning, a novel analytical approach revealed key differences between FM and non-FM student application materials. Findings suggest qualitative application data may contain identifiable thematic differences when comparing students who eventually match into FM residency programs to those who match into other specialties. Assessing student potential for FM could help guide recruitment and mentorship activities.