25 resultados para Medical errors

em Université de Montréal, Canada


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Objectifs: Les patients hospitalisés aux soins intensifs (SI) sont souvent victimes d’erreurs médicales. La nature interprofessionnelle des équipes de SI les rend vulnérables aux erreurs de communication. L’objectif primaire du projet est d’améliorer la communication dans une équipe interprofessionnelle de soins intensifs par une formation en simulation à haute fidélité. Méthodologie Une étude prospective randomisée contrôlée à double insu a été réalisée. Dix équipes de six professionnels de SI ont complété trois scénarios de simulations de réanimation. Le groupe intervention était débreffé sur des aspects de communication alors que le groupe contrôle était débreffé sur des aspects techniques de réanimation. Trois mois plus tard, les équipes réalisaient une quatrième simulation sans débreffage. Les simulations étaient toutes évaluées pour la qualité, l’efficacité de la communication et le partage des informations critiques par quatre évaluateurs. Résultats Pour l’issue primaire, il n’y a pas eu d’amélioration plus grande de la communication dans le groupe intervention en comparaison avec le groupe contrôle. Une amélioration de 16% de l’efficacité des communications a été notée dans les équipes de soins intensifs indépendamment du groupe étudié. Les infirmiers et les inhalothérapeutes ont amélioré significativement l’efficacité de la communication après trois sessions. L’effet observé ne s’est pas maintenu à trois mois. Conclusion Une formation sur simulateur à haute fidélité couplée à un débreffage peut améliorer à court terme l’efficacité des communications dans une équipe interprofessionnelle de SI.

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The Standards Committee of the Veterinary Medical Libraries Section was appointed in May 2000 and charged to create standards for the ideal academic veterinary medical library, written from the perspective of veterinary medical librarians. The resulting Standards for the Academic Veterinary Medical Library were approved by members of the Veterinary Medical Libraries Section during MLA ’03 in San Diego, California. The standards were approved by Section Council in April 2005 and received final approval from the Board of Directors of the Medical Library Association during MLA ’04 in Washington, DC.

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This note investigates the adequacy of the finite-sample approximation provided by the Functional Central Limit Theorem (FCLT) when the errors are allowed to be dependent. We compare the distribution of the scaled partial sums of some data with the distribution of the Wiener process to which it converges. Our setup is purposely very simple in that it considers data generated from an ARMA(1,1) process. Yet, this is sufficient to bring out interesting conclusions about the particular elements which cause the approximations to be inadequate in even quite large sample sizes.

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In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

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This note develops general model-free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit the recent asymptotic distributional results in Barndorff-Nielsen and Shephard (2002a), are both easy to implement and highly accurate in empirically realistic situations. On properly accounting for the measurement errors in the volatility forecast evaluations reported in Andersen, Bollerslev, Diebold and Labys (2003), the adjustments result in markedly higher estimates for the true degree of return-volatility predictability.

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We consider entry-level medical markets for physicians in the United Kingdom. These markets experienced failures which led to the adoption of centralized market mechanisms in the 1960's. However, different regions introduced different centralized mechanisms. We advise physicians who do not have detailed information about the rank-order lists submitted by the other participants. We demonstrate that in each of these markets in a low information environment it is not beneficial to reverse the true ranking of any two acceptable hospital positions. We further show that (i) in the Edinburgh 1967 market, ranking unacceptable matches as acceptable is not profitable for any participant and (ii) in any other British entry-level medical market, it is possible that only strategies which rank unacceptable positions as acceptable are optimal for a physician.