Translational science is defined as the field of investigation focused on understanding the scientific and operational principles underlying each step of the translational process. Further development of the field is advanced by describing the key desirable characteristics of individuals who seek to uncover these principles to increase the efficiency and efficacy of translation. The members of Translation Together, a newly launched international collaborative effort to advance translational innovation, present here a consensus representation of the fundamental characteristics of a translational scientist. We invite all stakeholders to contribute in the ongoing efforts to develop the field and educate the next generation of translational scientists.
The outcomes of a new universal program aimed at preventing depressive symptoms and disorders in 8- to 9-year-old children are presented. The Positive Thinking Program is a mental health promotion program based on cognitive and behavioural strategies. It is designed to meet the developmental needs of children in the middle primary school Years 4 and 5. Four state primary schools were randomly assigned to receive the program implemented by psychologists or to a control condition involving their regular Health Education curriculum. Seventy-two children participated in the intervention condition and 48 children in the control condition. Children completed measures of depressive and anxiety symptomatology, depressive disorders, and attribution style. The intervention was associated with reductions in depressive symptoms and more positive attributions at post-intervention. Compared to the control group, there was a lower prevalence of depressive disorders at posttest and fewer intervention group children developed a depressive disorder at a 9-month follow-up.
Recent research has extended the metacognitive model of adult psychopathology to childhood anxiety, however the results have been confounded by poor comprehension of the Metacognitions Questionnaire for Children (MCQ-C) amongst 7-8 year olds. The aim of this study was to improve comprehension of the MCQ-C, to enable reliable and valid evaluation of the metacognitive model of anxiety in children. Poorly comprehended items of the MCQ-C were revised to the appropriate reading level and pilot tested with 7-8 year olds. One hundred and eighty seven children aged 7-12 years then completed an online version of the revised MCQ-C (MCQ-CR) and selfreport measures of anxiety symptoms, excessive worry and externalising thoughts. The MCQ-CR was well understood by children as young as 7 years and exhibited sound psychometric properties. As predicted, children's negative beliefs about worry, thoughts in general and memories were found to be significantly positively related to symptoms of anxiety disorders. Confirmatory factor analysis supported the construct validity of the scale. Positive beliefs about worry were not associated with children's worry levels, raising questions about the relevance of this element of the metacognitive model with children. Although further validation is required with a clinical sample, these results provide support for the integral role played by metacognitions in childhood anxiety disorders, and suggest that these mechanisms may be appropriate targets for future early intervention and treatment programs.
To be good conversational partners, natural language processing (NLP) systems should be trained to produce contextually useful utterances. Prior work has investigated training NLP systems with communication-based objectives, where a neural listener stands in as a communication partner. However, these systems commonly suffer from semantic drift where the learned language diverges radically from natural language. We propose a method that uses a population of neural listeners to regularize speaker training. We first show that language drift originates from the poor uncertainty calibration of a neural listener, which makes high-certainty predictions on novel sentences. We explore ensemble-and dropoutbased populations of listeners and find that the former results in better uncertainty quantification. We evaluate both population-based objectives on reference games, and show that the ensemble method with better calibration enables the speaker to generate pragmatic utterances while scaling to a large vocabulary and generalizing to new games and listeners. 1
An overarching goal of natural language processing is to enable machines to communicate seamlessly with humans. However, natural language can be ambiguous or unclear. In cases of uncertainty, humans engage in an interactive process known as repair: asking questions and seeking clarification until their uncertainty is resolved. We propose a framework for building a visually grounded questionasking model capable of producing polar (yesno) clarification questions to resolve misunderstandings in dialogue. Our model uses an expected information gain objective to derive informative questions from an off-the-shelf image captioner without requiring any supervised question-answer data. We demonstrate our model's ability to pose questions that improve communicative success in a goal-oriented 20 questions game with synthetic and human answerers.
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