University of Haifa

About: University of Haifa is a education organization based out in Haifa, Israel. It is known for research contribution in the topics: Population & Poison control. The organization has 7558 authors who have published 27141 publications receiving 711629 citations. The organization is also known as: Haifa University & Universiṭat Ḥefah.

Assessment of nursing home residents in Europe: the Services and Health for ELderly in Long TERm care (SHELTER) study

Abstract: Aims of the present study are the following: 1. to describe the rationale and methodology of the Services and Health for Elderly in Long TERm care (SHELTER) study, a project funded by the European Union, aimed at implementing the interRAI instrument for Long Term Care Facilities (interRAI LTCF) as a tool to assess and gather uniform information about nursing home (NH) residents across different health systems in European countries; 2. to present the results about the test-retest and inter-rater reliability of the interRAI LTCF instrument translated into the languages of participating countries; 3 to illustrate the characteristics of NH residents at study entry. A 12 months prospective cohort study was conducted in 57 NH in 7 EU countries (Czech Republic, England, Finland, France, Germany, Italy, The Netherlands) and 1 non EU country (Israel). Weighted kappa coefficients were used to evaluate the reliability of interRAI LTCF items. Mean age of 4156 residents entering the study was 83.4 ± 9.4 years, 73% were female. ADL disability and cognitive impairment was observed in 81.3% and 68.0% of residents, respectively. Clinical complexity of residents was confirmed by a high prevalence of behavioral symptoms (27.5% of residents), falls (18.6%), pressure ulcers (10.4%), pain (36.0%) and urinary incontinence (73.5%). Overall, 197 of the 198 the items tested met or exceeded standard cut-offs for acceptable test-retest and inter-rater reliability after translation into the target languages. The interRAI LTCF appears to be a reliable instrument. It enables the creation of databases that can be used to govern the provision of long-term care across different health systems in Europe, to answer relevant research and policy questions and to compare characteristics of NH residents across countries, languages and cultures.

Seven Myths About Emotional Intelligence

Abstract: Inspired by an influx of academic research, the writ ingof several best-selling trade texts, and frequent me dia exposure, emotional intelligence (El) has emerged recently as one of the most high profile of the psycholog ical constructs (Matthews, Zeidner, & Roberts, 2002). The concept has also prospered due both to cultural trends and orientations that stress the previously ne glected role of the emotions (with some claiming it con stitutes a zeitgeist) and to increasing efforts at standard ized assessment of individual differences in El (Mayer, Salovey & Caruso, 2000a). Common claims suggest that tests for El are predictive of important educational and( occupational criteria, beyond that proportion of variance that general intellectual ability predicts. Thus, the field has increasingly important implications for so ciety, particularly in the impetus to improve emotional fuinctioning in real life. Proponents of El claim that indi v iduals can enjoy happier and more fulfilled lives if they ar-e aware of both their own emotions and those of other people and able to regulate those emotions effectively. Another reason for the widespread, often uncritical, em bracing of the El construct is the suggestion that El gives hope for a more utopian, classless society. This vision for the future stands in contrast to research suggesting a preordained "cognitive elite" (Herrnstein & Murray, 1'994) because El is within anyone's realm to learn and cultivate. Goleman's (1995) widely read book claims that raising El is a panacea for all manner of psychologi cal and social problems. Despite the seeming importance of these claims, scientific investigation of a clearly identified construct of El is sparse. Many of the current propositions pre sented in bo h the populist and specialist literature have littl empirical substance (Matthews, Zeidner et al., 2002). Stripped of scientific trappings, it remains plausible that El is nothing but the latest in a long line of psychological fads. On the other hand, because sys tematic scientific research is just beginning, EI could ind ed mature into a construct that is theoretically me ningful, empirically important, and practically useful. In this article, we examine seven myths about El, that is, st ong, widely believed claims that purport edly give the concept of El scientific credibility. In each case, we identify weaknesses in evidence and ar gument that challenge the value of the El construct. Today's myth could become tomorrow's accepted wisdom, or it could be conclusively falsified. For each claim, we also evaluate the likelihood that it will even tually be substantiated by research. This article focuses on what we see as the key shortcomings of current work on El and the prospects for an eventual science of El. How ver, we also acknowledge the intrinsic inter est of the subject material and the various research ef for s inspired by the concept. Our aim is not to dismiss work on El out of hand, but to examine where the first wave of research on the construct is meeting barriers to progress, and whether those barriers can be overcome.

Automatic Discovery of Part-Whole Relations

Abstract: An important problem in knowledge discovery from text is the automatic extraction of semantic relations. This paper presents a supervised, semantically intensive, domain independent approach for the automatic detection of part-whole relations in text. First an algorithm is described that identifies lexico-syntactic patterns that encode part-whole relations. A difficulty is that these patterns also encode other semantic relations, and a learning method is necessary to discriminate whether or not a pattern contains a part-whole relation. A large set of training examples have been annotated and fed into a specialized learning system that learns classification rules. The rules are learned through an iterative semantic specialization (ISS) method applied to noun phrase constituents. Classification rules have been generated this way for different patterns such as genitives, noun compounds, and noun phrases containing prepositional phrases to extract part-whole relations from them. The applicability of these rules has been tested on a test corpus obtaining an overall average precision of 80.95% and recall of 75.91%. The results demonstrate the importance of word sense disambiguation for this task. They also demonstrate that different lexico-syntactic patterns encode different semantic information and should be treated separately in the sense that different clarification rules apply to different patterns.