Enabling Policy for Health and Social Co-ops in BC

This paper examines the role that co-operatives are playing in the provision of health and social services in Canada and internationally, and the impact of government policy, legislation, and operating procedure on the ability of co-operative models to provide these services in British Columbia, Canada. The paper also examines other factors - both internal and external to co-op organizations - that affect the capacity of co-ops to play a more meaningful role in the production and delivery of health and social care services. The paper proposes that there are effective alternatives to the prevailing view that health and social services must be supplied either by government or the private sector. A third alternative, a social economy model based on consumer control and operating at a community level through a variety of community based, non-profit, co-operative, and social enterprises has been breaking new ground and warrants serious attention by policy makers and legislators. A social economy approach also requires governments to move beyond the strict utilitarian view of public services that has come with the application of private sector management models to the public sector.BC-Alberta Social Economy Research Alliance; BC Co-operative Association; BC Institute for Co-operative Studies; VanCity Community Foundatio

Disrupted Sense of Agency as a State Marker of First-Episode Schizophrenia: A Large-Scale Follow-Up Study

Background: Schizophrenia is often characterized by a general disruption of self-processing and self-demarcation. Previous studies have shown that self-monitoring and sense of agency (SoA, i.e., the ability to recognize one's own actions correctly) are altered in schizophrenia patients. However, research findings are inconclusive in regards to how SoA alterations are linked to clinical symptoms and their severity, or cognitive factors. Methods: In a longitudinal study, we examined 161 first-episode schizophrenia patients and 154 controls with a continuous-report SoA task and a control task testing general cognitive/sensorimotor processes. Clinical symptoms were assessed with the Positive and Negative Syndrome Scale (PANSS). Results: In comparison to controls, patients performed worse in terms of recognition of self-produced movements even when controlling for confounding factors. Patients' SoA score correlated with the severity of PANSS-derived “Disorganized” symptoms and with a priori defined symptoms related to self-disturbances. In the follow-up, the changes in the two subscales were significantly associated with the change in SoA performance. Conclusion: We corroborated previous findings of altered SoA already in the early stage of schizophrenia. Decreased ability to recognize self-produced actions was associated with the severity of symptoms in two complementary domains: self-disturbances and disorganization. While the involvement of the former might indicate impairment in self-monitoring, the latter suggests the role of higher cognitive processes such as information updating or cognitive flexibility. The SoA alterations in schizophrenia are associated, at least partially, with the intensity of respective symptoms in a state-dependent manner

Stacked structure learning for lifted relational neural networks

Lifted Relational Neural Networks (LRNNs) describe relational domains using weighted first-order rules which act as templates for constructing feed-forward neural networks. While previous work has shown that using LRNNs can lead to state-of-the-art results in various ILP tasks, these results depended on hand-crafted rules. In this paper, we extend the framework of LRNNs with structure learning, thus enabling a fully automated learning process. Similarly to many ILP methods, our structure learning algorithm proceeds in an iterative fashion by top-down searching through the hypothesis space of all possible Horn clauses, considering the predicates that occur in the training examples as well as invented soft concepts entailed by the best weighted rules found so far. In the experiments, we demonstrate the ability to automatically induce useful hierarchical soft concepts leading to deep LRNNs with a competitive predictive power

Learning predictive categories using lifted relational neural networks

Lifted relational neural networks (LRNNs) are a flexible neural-symbolic framework based on the idea of lifted modelling. In this paper we show how LRNNs can be easily used to specify declaratively and solve learning problems in which latent categories of entities, properties and relations need to be jointly induced