Locating the evidence for children and young people social prescribing: Where to start? A scoping review protocol.

It is estimated that disruptions to life caused by the COVID-19 pandemic have led to an increase in the number of children and young people suffering from mental health issues globally. In England one in four children experienced poor mental health in 2022. Social prescribing is gaining traction as a systems-based approach, which builds upon person-centered methods, to refer children and young people with non-clinical mental health issues to appropriate community assets.

Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions.

Models that predict brain responses to stimuli provide one measure of understanding of a sensory system and have many potential applications in science and engineering. Deep artificial neural networks have emerged as the leading such predictive models of the visual system but are less explored in audition. Prior work provided examples of audio-trained neural networks that produced good predictions of auditory cortical fMRI responses and exhibited correspondence between model stages and brain regions, but left it unclear whether these results generalize to other neural network models and, thus, how to further improve models in this domain.

Evaluating the implementation of a person-centred transition programme for adolescents and young adults with long-term conditions: the role of context and organisational behaviour.

Purpose: Drawing on the experiences of healthcare professionals in one paediatric hospital, this paper explores the influence of context and organisational behaviour on the implementation of a person-centred transition programme for adolescents and young adults (AYA) with long-term conditions.

Design/methodology/approach: A single embedded qualitative case study design informed by a realist evaluation framework, was used. Participants who had experience of implementing the transition programme were recruited from across seven individual services within the healthcare organisation.

Model metamers reveal divergent invariances between biological and artificial neural networks.

Deep neural network models of sensory systems are often proposed to learn representational transformations with invariances like those in the brain. To reveal these invariances, we generated 'model metamers', stimuli whose activations within a model stage are matched to those of a natural stimulus. Metamers for state-of-the-art supervised and unsupervised neural network models of vision and audition were often completely unrecognizable to humans when generated from late model stages, suggesting differences between model and human invariances.

Unveiling the benefits of multitasking in disentangled representation formation.

Johnston and Fusi recently investigated the emergence of disentangled representations when a neural network was trained to perform multiple simultaneous tasks. Such experiments explore the benefits of flexible representations and add to a growing field of research investigating the representational geometry of artificial and biological neural networks.