786 research outputs found

    Texture similarity estimation using contours

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    Reasons for non-vaccination: Parental vaccine hesitancy and the childhood influenza vaccination school pilot programme in England.

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    INTRODUCTION: In 2013, the annual influenza immunisation programme in England was extended to children to reduce the burden of influenza, but uptake was sub-optimal at 53.2%. AIM: To explore the reasons some parents decided not to vaccinate their child against influenza as part of the pilot programme offered in schools. METHODS: Cross-sectional qualitative study conducted between February and July 2015. 913 parents whose children were not vaccinated against influenza in the school pilots in West Yorkshire and Greater Manchester, England, were asked to comment on their reasons for non-vaccination and invited to take part in a semi-structured interview. 138 parents returned response forms, of which 38 were eligible and interested in participating and 25 were interviewed. Interview transcripts were coded by theme in NVivo. RESULTS: A third of parents who returned response forms had either vaccinated their child elsewhere, intended to have them vaccinated, or had not vaccinated them due to medical reasons (valid or perceived). Most interviewees were not convinced of the need to vaccinate their child against influenza. Parents expressed concerns about influenza vaccine effectiveness and vaccine side effects. Several parents interviewed declined the vaccine for faith reasons due to the presence of porcine gelatine in the vaccine. CONCLUSIONS: To significantly decrease the burden of influenza in England, influenza vaccination coverage in children needs to be >60%. Hence, it is important to understand the reasons why parents are not vaccinating their children, and to tailor the communication and immunisation programme accordingly. Our finding that a third of parents, who did not consent to their child being vaccinated as part of the school programme, had actually vaccinated their child elsewhere, intended to have their child vaccinated, or had not vaccinated them due to medical reasons, illustrates the importance of including additional questions or data sources when investigating under-vaccination

    Human search for a target on a textured background is consistent with a stochastic model

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    Previous work has demonstrated that search for a target in noise is consistent with the predictions of the optimal search strategy, both in the spatial distribution of fixation locations and in the number of fixations observers require to find the target. In this study we describe a challenging visual-search task and compare the number of fixations required by human observers to find the target to predictions made by a stochastic search model. This model relies on a target-visibility map based on human performance in a separate detection task. If the model does not detect the target, then it selects the next saccade by randomly sampling from the distribution of saccades that human observers made. We find that a memoryless stochastic model matches human performance in this task. Furthermore, we find that the similarity in the distribution of fixation locations between human observers and the ideal observer does not replicate: Rather than making the signature doughnut-shaped distribution predicted by the ideal search strategy, the fixations made by observers are best described by a central bias. We conclude that, when searching for a target in noise, humans use an essentially random strategy, which achieves near optimal behavior due to biases in the distributions of saccades we have a tendency to make. The findings reconcile the existence of highly efficient human search performance with recent studies demonstrating clear failures of optimality in single and multiple saccade tasks

    Challenges in Collaborative HRI for Remote Robot Teams

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    Collaboration between human supervisors and remote teams of robots is highly challenging, particularly in high-stakes, distant, hazardous locations, such as off-shore energy platforms. In order for these teams of robots to truly be beneficial, they need to be trusted to operate autonomously, performing tasks such as inspection and emergency response, thus reducing the number of personnel placed in harm's way. As remote robots are generally trusted less than robots in close-proximity, we present a solution to instil trust in the operator through a `mediator robot' that can exhibit social skills, alongside sophisticated visualisation techniques. In this position paper, we present general challenges and then take a closer look at one challenge in particular, discussing an initial study, which investigates the relationship between the level of control the supervisor hands over to the mediator robot and how this affects their trust. We show that the supervisor is more likely to have higher trust overall if their initial experience involves handing over control of the emergency situation to the robotic assistant. We discuss this result, here, as well as other challenges and interaction techniques for human-robot collaboration.Comment: 9 pages. Peer reviewed position paper accepted in the CHI 2019 Workshop: The Challenges of Working on Social Robots that Collaborate with People (SIRCHI2019), ACM CHI Conference on Human Factors in Computing Systems, May 2019, Glasgow, U

    Professor Martin Barratt 1936–2014

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