Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
-
Ensuring fair, equitable scheduling of faculty who work 24-hour, 7-day-per-week (24/7) clinical coverage is a challenge for academic emergency medicine (EM). Because most emergency department care is at personally valuable times (evenings, weekends, nights), optimizing clinical work is essential for the academic mission. To evaluate schedule fairness, the authors developed objective criteria for stress of the schedule, modified the schedule to improve equality, and evaluated faculty perceptions. They hypothesized that improved equality would increase faculty satisfaction. ⋯ Faculty perceived no improvement despite scheduling modifications that improved equality of the schedule and provided objective measures. Other predictors of stress, fairness, and satisfaction with the demanding clinical schedule must be identified to ensure the success of EM faculty.
-
Many trauma centers use mainly physiologic, first-tier criteria and mechanism-related, second-tier criteria to determine whether and at what level to activate a multidisciplinary trauma team in response to an out-of-hospital call. Some of these criteria result in a large number of unnecessary team activations while identifying only a few additional patients who require immediate operative intervention. ⋯ The four least predictive second-tier, mechanism-related criteria added little sensitivity to the trauma team activation rule at the cost of substantially decreased specificity, and they should be modified or eliminated. The first-tier, mainly physiologic criteria were all useful in predicting the need for an immediate multidisciplinary response. If increased specificity of the first-tier criteria is desired, the first criterion to eliminate is "age > 65."
-
The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. ⋯ Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.
-
Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. ⋯ Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.