Clinical experience as evidence in evidence-based practice

Background. This paper's starting point is the recognition (descriptive not normative) that, for the vast majority of day-to-day clinical decision-making situations, the 'evidence' for decision-making is experiential knowledge. Moreover, reliance on this knowledge base means that nurses must use cognitive shortcuts or heuristics for handling information when making decisions. These heuristics encourage systematic biases in decision-makers and deviations from the normative rules of 'good' decision-making. Aims. The aim of the paper is to explore three common heuristics and the biases that arise when handling complex information in clinical decision-making (overconfidence, hindsight and base rate neglect) and, in response to these biases, to illustrate some simple techniques for reducing the negative influence of heuristics. Discussion. Nurses face a limited range of types of uncertainty in their clinical decisions and draw primarily on experiential knowledge to handle these uncertainties. This paper argues that experiential knowledge is a necessary but not sufficient basis for clinical decision-making. It illustrates how overconfidence in one's knowledge base, being correct 'after the event' or with the benefit of hindsight, and ignoring the base rates associated with events, conditions or health states, can impact on professional judgements and decisions. The paper illustrates some simple strategies for minimizing the impact of heuristics on the real-life clinical decisions of nurses. Conclusion. The paper concludes that more research knowledge of the impact of heuristics and techniques to combat them in nursing decisions is needed

A conceptual treadmill: the need for ‘middle ground’ in clinical decision making theory in nursing

This paper explores the two predominant theoretical approaches to the process of nurse decision making prevalent within the nursing research literature: systematic-positivistic approaches as exemplifed by information processing theory, and the intuitive-humanistic approach of Patricia Benner. The two approaches' strengths and weaknesses are explored and as a result a third theoretical stance is proffered: the idea of a cognitive continuum. According to this approach the systematic and intuitive theoretical camps occupy polar positions at either end of a continuum as opposed to separate theoretical planes. The methodological and professional benefits of adopting such a stance are also briefly outlined

Acquiring Word-Meaning Mappings for Natural Language Interfaces

This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of phrases paired with meaning representations. WOLFIE is part of an integrated system that learns to transform sentences into representations such as logical database queries. Experimental results are presented demonstrating WOLFIE's ability to learn useful lexicons for a database interface in four different natural languages. The usefulness of the lexicons learned by WOLFIE are compared to those acquired by a similar system, with results favorable to WOLFIE. A second set of experiments demonstrates WOLFIE's ability to scale to larger and more difficult, albeit artificially generated, corpora. In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, most results to date for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to semantic lexicons. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance

The Integral Role of Tulsa Community College in the Mathematics and Science Preparation of Prospective Teachers

The role of the two-year college in the mathematics and science preparation of prospective teachers is fast becoming a prominent influence on teacher education programs across the country. This article describes the multifaceted role of Tulsa Community College (TCC), Tulsa, Oklahoma, in the preparation of prospective teachers in math and science. Since 1987 Tulsa Community College has hosted many events, activities, and programs aimed at the sciences. TCC activities/programs have focused on five areas: (1) preservice and inservice preparation; (2) summer teacher institutes supported by state and federal grants; (3) recruitment and emphasis on underrepresented groups; (4) parateacher associate degree/certification program; (5) workshops, seminars, and other activities. This article presents the TCC role by examining these five areas in terms of assessment of successful strategies, significant collaborations, and impact of the TCC teacher preparation activities on students and the community. Also presented are the implications for future TCC programs and the TCC vision for the math and science preparation of prospective teachers for the 21st century and beyond

Commentary on: Curley LJ, Munro J, Lages M, MacLean R, Murray J. Assessing cognitive bias in forensic decisions: a review and outlook. J Forensic Sci doi: 10.1111/1556-4029.14220. Epub 2019 Nov 6.

In their recent critical review titled “Assessing Cognitive Bias in Forensic Decisions: A Review and Outlook,” Curley et al. 1 offer a confused and incomplete discussion of “task relevance” in forensic science. Their failure to adopt a clear and appropriate definition of “task relevance” undermines the central conclusion of their article—the assertion that it is not necessarily an error for forensic scientists to rely on task-irrelevant information and that “task-irrelevant contextual information may sometimes aid forensic decision makers.” This conceptual flaw in the article becomes clear when we define “task relevance” appropriately, in the manner it was defined by the U.S. National Commission on Forensic Science 2. The Commission’s definition provides a bright-line standard for distinguishing contextual information that is helpful and should be considered from contextual information that is unhelpful and should not be considered. Once that matter is clarified, it becomes possible to discuss intelligently whether steps should be taken to minimize examiners’ exposure to task-irrelevant information in order to reduce the potential for contextual bias.This letter to the editor is published as Thompson, William C. "Commentary on: Curley LJ, Munro J, Lages M, MacLean R, Murray J. Assessing cognitive bias in forensic decisions: a review and outlook. J Forensic Sci." Journal of forensic sciences 65, no. 2 (2020): 666-667. Posted with permission of CSAFE.</p

Measurements of alpha_s from hadronic event shapes in e+e- annihilation

New studies of hadronic event shape observables in e+ e- collisions between 13 and 183 GeV CM energy have enabled the running of alpha_s to be confirmed and the validity of non-perturbative power-law corrections to be investigated. A more precise value of alpha_s(M_Z) with reduced theoretical errors has been reported from fitting 18 oriented event shape distributions measured in one experiment at the Z.Comment: 3 pages, 2 figures, ICHEP Vancouver, Canada, 199

Study design and mark-recapture estimates of dispersal: A case study with the endangered damselfly Coenagrion mercuriale

Accurate data on dispersal ability are vital to the understanding of how species are affected by fragmented landscapes. However, three factors may limit the ability of field studies to detect a representative sample of dispersal events: (1) the number of individuals monitored, (2) the area over which the study is conducted and (3) the time over which the study is conducted. Using sub-sampling of mark-release-recapture data from a study on the endangered damselfly Coenagrion mercuriale (Charpentier), we show that maximum dispersal distance is strongly related to the number of recaptured individuals in the mark-release-recapture study and the length of time over which the study is conducted. Median dispersal distance is only related significantly to the length of the study. Spatial extent is not associated with either dispersal measure in our analysis. Previously consideration has been given to the spatial scale of dispersal experiments but we demonstrated conclusively that temporal scale and the number of marked individuals also have the potential to affect the measurement of dispersal. Based on quadratic relationships between the maximum dispersal distance, recapture number and length of study, we conclude that a previous study was of sufficient scale to characterise the dispersal kernel of C. mercuriale. Our method of analysis could be used to ensure that the results of mark-release-recapture studies are independent of levels of spatial and temporal investment. Improved confidence in dispersal estimates will enable better management decisions to be made for endangered species

Modelling of selection and mating decisions in tree breeding programs

Hardwood trees from the temperate forests of southern Australia are an important source of timber for high quality paper. Two species in particular, Eucalyptus globulus and Eucalyptus nitens are well suited to this purpose and are now widely grown in commercial plantations. These plantations have been established by professional tree breeders using seedlings derived originally from broadly based collection of seed in natural forests. To increase productivity it is desirable to select trees that grow quickly and give high yields of top quality timber. Nevertheless it is important to maintain genetic diversity in the breeding population and thereby retain a robust capacity to adapt to changing environmental factors. In this article we formulate a number of related mathematical models for the selection and mating processes and discuss the consequences of these models. We recommend a relatively simple scheme which can be implemented on an IBM compatible PC using standard algorithms