Due to the growing awareness of exercise related arrhythmias and improved sensitivity of diagnostic modalities, physicians are increasingly faced with choices that may have life changing impact for the athlete. This article surveys recent research and expert opinion addressing benign and pathogenic cardiac changes underlying arrhythmias in athletes.
Purpose:To evaluate the effectiveness of a learner-centered, simulation-based training developed to help medical students improve their procedural skills in intubation, arterial line placement, lumbar puncture, and central line insertion.Method:The study participants were second and third year medical students. Anesthesiology residents provided the training and evaluated students’ procedural skills. Two residents were present at each station to train the medical students who rotated through all 4 stations. Pre/posttraining assessment of confidence, knowledge, and procedural skills was done using a survey, a multiple-choice test, and procedural checklists, respectively.Results:In total, 24 students were trained in six 4-hour sessions. Students reported feeling significantly more confident, after training, in performing all 4 procedures on a real patient (P < .001). Paired-samples t tests indicated statistically significant improvement in knowledge scores for intubation, t(23) = −2.92, P < .001, and arterial line placement, t(23) = −2.75, P < .001. Procedural performance scores for intubation (t(23) = −17.29, P < .001), arterial line placement (t(23) = −19.75, P < .001), lumbar puncture (t(23) = −16.27, P < .001), and central line placement (t(23) = −17.25, P < .001) showed significant improvement. Intraclass correlation coefficients indicated high reliability in checklist scores for all procedures.Conclusions:The simulation sessions allowed each medical student to receive individual attention from 2 residents for each procedure. Students’ written comments indicated that this training modality was well received. Results showed that medical students improved their self-confidence, knowledge, and skills in the aforementioned procedures.
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HE NEED for manpower forecasting at the national, T state, and regional levels has been increasingly recognized over the past several decades. The rising concern with manpower forecasting was significantly enhanced with the passage of manpower legislation during the Kennedy-Johnson era which set forth a national commitment t o education and training programs designed to adjust to changing labor market conditions. Furthermore, the decentralization of the manpowerplanning process brought on by the revenue-sharing provisions of the Comprehensive Employment and Training Act of 1973 has sharply increased the need for occupational employment projections at the state and local levels. In order t o facilitate and encourage the construction of manpower forecasts by state and regional planners, the U.S. Bureau of Labor Statistics (BLS) has developed a basic methodology to forecast state and area manpower needs. This methodology is set forth in Tomorrow's Manpower Needs, and it has become exceedingly popular with regional manpower planners.' Recently, the Manpower Administration, the BLS, and the state employment services have initiated several new progranis to improve the data bases from which manpower forecasts are derived. However, it appears that the basic methodologies for forecasting will remain largely unchanged .'The purpose of this article is to suggest an alternative forecasting methodology to that which is currently in vogue. The methodology set forth here offers local area manpower practitioners a model which is both more flexible and less expensive than the BLS procedure. Furthermore, this alternative model can be applied advantageously to statewide employment forecasting.The first section of this article critically examines BLS industrial projection procedures. The second section sets forth an alternative model for forecasting area employment by industry. The third section illustrates the model using data for the Greensboro, North Carolina labor market area. The final section discusses alternative techniques for generating forecasts of occupational demands from industry employment projections. The BLS ProcedureIn order to forecast local employment by industrial group, the BLS recommends use of the following regression model^.^(1)E, = a t bT, orwhere Ei is area employment in industry i, Ni is national employment in industry i, T is time, and X stands for a set of variables representing area population, per capita change in income, or other relevant area indicators.Equation 1 is a linear time trend extrapolation which is devoid of theoretical content since it in effect assumes that the forces affecting area employment in the past will continue to do so with the same intensity in the future. This model can be expected to yield poor shortrun and long-run forecasts since it blindly projects past trends. No variables are included which would take account of the effect of changes in the structure of local, regional, and national demand on area employment. Equation 2 has slightly more potential for industrial sectors ...
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