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Publications
Family‐centred care interventions for children with chronic conditions: A scoping review
Children with chronic conditions have greater health care needs than the general paediatric population but may not receive care that centres… (voir plus) their needs and preferences as identified by their families. Clinicians and researchers are interested in developing interventions to improve family‐centred care need information about the characteristics of existing interventions, their development and the domains of family‐centred care that they address. We conducted a scoping review that aimed to identify and characterize recent family‐centred interventions designed to improve experiences with care for children with chronic conditions.
Children with chronic conditions have greater health care needs than the general paediatric population but may not receive care that centres… (voir plus) their needs and preferences as identified by their families. Clinicians and researchers are interested in developing interventions to improve family‐centred care need information about the characteristics of existing interventions, their development and the domains of family‐centred care that they address. We conducted a scoping review that aimed to identify and characterize recent family‐centred interventions designed to improve experiences with care for children with chronic conditions.
Children with chronic conditions have greater health care needs than the general paediatric population but may not receive care that centres… (voir plus) their needs and preferences as identified by their families. Clinicians and researchers are interested in developing interventions to improve family‐centred care need information about the characteristics of existing interventions, their development and the domains of family‐centred care that they address. We conducted a scoping review that aimed to identify and characterize recent family‐centred interventions designed to improve experiences with care for children with chronic conditions.
Generative Flow Networks (GFlowNets, GFNs) are a generative framework for learning unnormalized probability mass functions over discrete spa… (voir plus)ces. Since their inception, GFlowNets have proven to be useful for learning generative models in applications where the majority of the discrete space is unvisited during training. This has inspired some to hypothesize that GFlowNets, when paired with deep neural networks (DNNs), have favourable generalization properties. In this work, we empirically verify some of the hypothesized mechanisms of generalization of GFlowNets. In particular, we find that the functions that GFlowNets learn to approximate have an implicit underlying structure which facilitate generalization. We also find that GFlowNets are sensitive to being trained offline and off-policy; however, the reward implicitly learned by GFlowNets is robust to changes in the training distribution.
