9,077 research outputs found
Binding an event to its source at encoding improves children\u27s source monitoring
Children learn information from a variety of sources and often remember the content but forget the source. While the majority of research has focused on retrieval mechanisms for such difficulties, the present investigation examines whether the way in which sources are encoded influences future source monitoring. In Study 1, 86 children aged 3 to 8 years participated in two photography sessions on different days. Children were randomly assigned to either the Difference condition (they were asked to pay attention to differences between the two events), the Memory control condition (asked to pay attention with no reference to differences), or the No-Instruction control (no special instructions were given). One week later, during a structured interview about the photography session, the 3-4 year-olds in the No-Instruction condition were less accurate and responded more often with \u27don\u27t know\u27 than the 7-8 year-olds. However, the older children in the Difference condition made more source confusions than the younger children suggesting improved memory for content but not source. In Study 2, the Difference condition was replaced by a Difference-Tag condition where details were pointed out along with their source (i.e., tagging source to content). Ninety-four children aged 3 to 8 years participated. Children in the Difference-Tag condition made fewer source-monitoring errors than children in the Control condition. The results of these two studies together suggest that binding processes at encoding can lead to better source discrimination of experienced events at retrieval and may underlie the rapid development of source monitoring in this age range
Access to health services in Western Newfoundland, Canada: Issues, barriers and recommendations emerging from a community-engaged research project
Research indicates that people living in rural and remote areas of Canada face challenges to accessing health services. This article reports on a community-engaged research project conducted by investigators at Memorial University of Newfoundland in collaboration with the Rural Secretariat Regional Councils and Regional Partnership Planners for the Corner Brook–Rocky Harbour and Stephenville–Port aux Basques Rural Secretariat Regions of Newfoundland and Labrador. The aim of this research was to gather information on barriers to accessing health services, to identify solutions to health services’ access issues and to inform policy advice to government on enhancing access to health services. Data was collected through: (1) targeted distribution of a survey to communities throughout the region, and (2) informal ‘kitchen table’ discussions to discuss health services’ access issues. A total of 1049 surveys were collected and 10 kitchen table discussions were held. Overall, the main barriers to care listed in the survey included long wait times, services not available in the area and services not available at time required. Other barriers noted by survey respondents included transportation problems, financial concerns, no medical insurance coverage, distance to travel and weather conditions. Some respondents reported poorer access to maternal/child health and breast and cervical screening services and a lack of access to general practitioners, pharmacy services, dentists and nurse practitioners. Recommendations that emerged from this research included improving the recruitment of rural physicians, exploring the use of nurse practitioners, assisting individuals with travel costs, developing specialist outreach services, increasing use of telehealth services and initiating additional rural and remote health research.Keywords: rural, remote, healthcare, health services, social determinants of healt
Hyperlipasemia in dogs with acute kidney injury treated with and without hemodialysis
Hyperlipasemia has been reported in dogs with acute kidney injury (AKI) treated with and without hemodialysis (HD) but associations with AKI severity, treatment modality, and outcome have not been extensively evaluated.
Retrospective study including 125 client-owned dogs with AKI, with creatinine concentrations and 1,2-o-dilauryl-rac-glycero-3-glutaric acid-(6’-methyresorufin) ester (DGGR) lipase activities measured within 24 hours of admission and during hospitalization. Dogs with a history of acute (AP) or chronic pancreatitis were excluded.
DGGR-lipase activity >3x upper reference limit (URL) was found in 28.8% and 57.5% of dogs at admission and during hospitalization, respectively, and severe hyperlipasemia (>10x URL) was seen in 34% of dogs during hospitalization. A diagnosis of AP was given to 8.8% and 16% of dogs at admission and during hospitalization, respectively. DGGR-lipase activity was higher in dogs with International Renal Interest Society (IRIS) grades 4–5 than in those with grades 1–3, but no correlation was found between DGGR-lipase activity and creatinine concentrations. Treatment with HD was not associated with hyperlipasemia independently of IRIS group. Severe AKI (IRIS 4–5) and high DGGR-lipase activity were associated with poor outcome.
Hyperlipasemia is frequent in dogs with AKI, and is associated with severity of AKI and death, but not independently with HD treatment. Further studies are needed to evaluate causes of hyperlipasemia in dogs with AKI
Cultivation and characterization of sex-specific microbiota in the broadnosed pipefish Syngnathus typhle
Within-group fairness: A guidance for more sound between-group fairness
As they have a vital effect on social decision-making, AI algorithms not only
should be accurate and but also should not pose unfairness against certain
sensitive groups (e.g., non-white, women). Various specially designed AI
algorithms to ensure trained AI models to be fair between sensitive groups have
been developed. In this paper, we raise a new issue that between-group fair AI
models could treat individuals in a same sensitive group unfairly. We introduce
a new concept of fairness so-called within-group fairness which requires that
AI models should be fair for those in a same sensitive group as well as those
in different sensitive groups. We materialize the concept of within-group
fairness by proposing corresponding mathematical definitions and developing
learning algorithms to control within-group fairness and between-group fairness
simultaneously. Numerical studies show that the proposed learning algorithms
improve within-group fairness without sacrificing accuracy as well as
between-group fairness
The Future of e-Learning in Medical Education: Current Trend and Future Opportunity
A wide range of e-learning modalities are widely integrated in medical education. However, some of the key questions related to the role of e-learning remain unanswered, such as (1) what is an effective approach to integrating technology into pre-clinical vs. clinical training?; (2) what evidence exists regarding the type and format of e-learning technology suitable for medical specialties and clinical settings?; (3) which design features are known to be effective in designing on-line patient simulation cases, tutorials, or clinical exams?; and (4) what guidelines exist for determining an appropriate blend of instructional strategies, including on-line learning, face-to-face instruction, and performance-based skill practices? Based on the existing literature and a variety of e-learning examples of synchronous learning tools and simulation technology, this paper addresses the following three questions: (1) what is the current trend of e-learning in medical education?; (2) what do we know about the effective use of e-learning?; and (3) what is the role of e-learning in facilitating newly emerging competency-based training? As e-learning continues to be widely integrated in training future physicians, it is critical that our efforts in conducting evaluative studies should target specific e-learning features that can best mediate intended learning goals and objectives. Without an evolving knowledge base on how best to design e-learning applications, the gap between what we know about technology use and how we deploy e-learning in training settings will continue to widen
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