基于学分绩点与综合素质测评的换算模型建立

学生综合素质测评体系的完善对有效实施大学生综合素质测评工作以及指导大学生健康发展有重要意义,其中,学生综合素质测评体系的关键在于学分绩点与综合素质测评的换算模型的转化。通过分析高校现行学分绩点测评公式,发现其存在的主要缺陷是转化公式不满足统计学原理,并指出学分绩点测评的换算公式应建立在对特定学生群体特征参数群数值分析的基础上。以西南石油大学某专业学生某学年的成绩分布数据进行验证,采取统计原理、常数变异法和不动点原理进行建模,利用求导方法对学分绩点测评中的特征参数群进行层次划分,然后对每段划分积分,通过归一化来确定变异项,得出新的换算模型。结果表明新模型能更好地反映学生的学习成绩在综合素质测评中的地位。     

Evaluation of Inhaled Versus Deposited Dose Using the Exponential Dose‐Response Model for Inhalational Anthrax in Nonhuman Primate,Rabbit, and Guinea Pig

The application of the exponential model is extended by the inclusion of new nonhuman primate (NHP), rabbit, and guinea pig dose‐lethality data for inhalation anthrax. Because deposition is a critical step in the initiation of inhalation anthrax, inhaled doses may not provide the most accurate cross‐species comparison. For this reason, species‐specific deposition factors were derived to translate inhaled dose to deposited dose. Four NHP, three rabbit, and two guinea pig data sets were utilized. Results from species‐specific pooling analysis suggested all four NHP data sets could be pooled into a single NHP data set, which was also true for the rabbit and guinea pig data sets. The three species‐specific pooled data sets could not be combined into a single generic mammalian data set. For inhaled dose, NHPs were the most sensitive (relative lowest LD₅₀) species and rabbits the least. Improved inhaled LD₅₀s proposed for use in risk assessment are 50,600, 102,600, and 70,800 inhaled spores for NHP, rabbit, and guinea pig, respectively. Lung deposition factors were estimated for each species using published deposition data from Bacillus spore exposures, particle deposition studies, and computer modeling. Deposition was estimated at 22%, 9%, and 30% of the inhaled dose for NHP, rabbit, and guinea pig, respectively. When the inhaled dose was adjusted to reflect deposited dose, the rabbit animal model appears the most sensitive with the guinea pig the least sensitive species.     

Small area estimation strategies for large population surveys: a comparison of design and model-based methods

Small area estimation (SAE) concerns with how to reliably estimate population quantities of interest when some areas or domains have very limited samples. This is an important issue in large population surveys, because the geographical areas or groups with only small samples or even no samples are often of interest to researchers and policy-makers. For example, large population health surveys, such as Behavioural Risk Factor Surveillance System and Ohio Mecaid Assessment Survey (OMAS), are regularly conducted for monitoring insurance coverage and healthcare utilization. Classic approaches usually provide accurate estimators at the state level or large geographical region level, but they fail to provide reliable estimators for many rural counties where the samples are sparse. Moreover, a systematic evaluation of the performances of the SAE methods in real-world setting is lacking in the literature. In this paper, we propose a Bayesian hierarchical model with constraints on the parameter space and show that it provides superior estimators for county-level adult uninsured rates in Ohio based on the 2012 OMAS data. Furthermore, we perform extensive simulation studies to compare our methods with a collection of common SAE strategies, including direct estimators, synthetic estimators, composite estimators, and Datta GS, Ghosh M, Steorts R, Maples J.'s [Bayesian benchmarking with applications to small area estimation. Test 2011;20(3):574–588] Bayesian hierarchical model-based estimators. To set a fair basis for comparison, we generate our simulation data with characteristics mimicking the real OMAS data, so that neither model-based nor design-based strategies use the true model specification. The estimators based on our proposed model are shown to outperform other estimators for small areas in both simulation study and real data analysis.     

Foodborne Exposure to Pesticides and Methylmercury in the United Arab Emirates

As part of a comprehensive environmental health strategic planning project initiated by the government of Abu Dhabi, we assessed potential dietary exposure in the United Arab Emirates (UAE) to methylmercury (in seafood) and pesticides (in fruits and vegetables) above international guideline levels. We present results for the UAE population by age, gender, and body mass index. Our results show very low daily risks of exposure to pesticides in fruits and vegetables at levels exceeding WHO guidelines even under the conservative assumption that no pesticides are removed during washing and food preparation. Thus, exposure to pesticides on fruits and vegetables does not appear to be a major public health concern in the UAE. The chances of exposure to methylmercury in seafood are much higher; our model estimates a mean 1 in 5 daily risk of exceeding the FAO/WHO provisional tolerable weekly intake. However, great caution should be used in interpreting these results, as we analyzed only the risks and not the substantial benefits of fish consumption. In fact, previous studies have demonstrated that exposure to the n‐3 polyunsaturated fatty acids in fish can increase IQ in developing children, and it can substantially decrease the risk in adults of coronary heart disease and stroke. Further research is warranted to compare the risk of Me‐Hg exposure from fish to the nutritional benefits of fish consumption in the UAE and to determine appropriate methods to communicate risk and benefit information to the UAE population.     

Bayesian Treatment of Model Uncertainty for Partially Applicable Models

This article discusses how analyst's or expert's beliefs on the credibility and quality of models can be assessed and incorporated into the uncertainty assessment of an unknown of interest. The proposed methodology is a specialization of the Bayesian framework for the assessment of model uncertainty presented in an earlier paper. This formalism treats models as sources of information in assessing the uncertainty of an unknown, and it allows the use of predictions from multiple models as well as experimental validation data about the models’ performances. In this article, the methodology is extended to incorporate additional types of information about the model, namely, subjective information in terms of credibility of the model and its applicability when it is used outside its intended domain of application. An example in the context of fire risk modeling is also provided.     

A longitudinal comparative study of the impact of the experience of social work education on interpersonal and critical thinking capabilities

Head (the intellectual/critical thinking) and Heart (the emotional-interpersonal) are long established twin-pillars of social work. Their importance is not just reflected in literature devoted to each but in the professional proficiencies and capabilities expected for practice. While clearly intertwined—both operate together in practice—and central to practice proficiencies/capabilities, implying implicit measurement of standards, there is a curious lack of research focusing on measurement of the two together. This novel study is the first to carry out a longitudinal comparative study examining the extent to which interpersonal and critical thinking capabilities are developed during the lifetime of qualifying social work training as measured through programme performance and development. It focuses on four cohorts (n = 170) at two Universities (Undergraduate and Masters). While findings show the interpersonal significantly underpinned performance and outcome, critical thinking—though embedded in Masters programme performance—was less significant, particularly in the undergraduate programmes. The study concludes that we cannot assume interpersonal and critical thinking capabilities are always incorporated into the learning process and attention should be paid to the overt measurement of interpersonal and critical thinking capabilities together with the processes and mechanisms by which they are focused on in programmes.     

New Data,Strategies, and Insights for Listeria monocytogenes Dose‐Response Models: Summary of an Interagency Workshop, 2011

Listeria monocytogenes is a leading cause of hospitalization, fetal loss, and death due to foodborne illnesses in the United States. A quantitative assessment of the relative risk of listeriosis associated with the consumption of 23 selected categories of ready‐to‐eat foods, published by the U.S. Department of Health and Human Services and the U.S. Department of Agriculture in 2003, has been instrumental in identifying the food products and practices that pose the greatest listeriosis risk and has guided the evaluation of potential intervention strategies. Dose‐response models, which quantify the relationship between an exposure dose and the probability of adverse health outcomes, were essential components of the risk assessment. However, because of data gaps and limitations in the available data and modeling approaches, considerable uncertainty existed. Since publication of the risk assessment, new data have become available for modeling L. monocytogenes dose‐response. At the same time, recent advances in the understanding of L. monocytogenes pathophysiology and strain diversity have warranted a critical reevaluation of the published dose‐response models. To discuss strategies for modeling L. monocytogenes dose‐response, the Interagency Risk Assessment Consortium (IRAC) and the Joint Institute for Food Safety and Applied Nutrition (JIFSAN) held a scientific workshop in 2011 (details available at http://foodrisk.org/irac/events/ ). The main findings of the workshop and the most current and relevant data identified during the workshop are summarized and presented in the context of L. monocytogenes dose‐response. This article also discusses new insights on dose‐response modeling for L. monocytogenes and research opportunities to meet future needs.     

A Comparative Analysis and Evaluation of a University Employee Assistance Program

Abstract

The major goal of this presentation is to provide overview of evaluation results of the University of Pittsburgh (Pitt) Faculty and Staff Assistance Program (FSAP). The major goal of the Pitt FSAP evaluation was to generate information pertaining to the program's service adequacy, program effectiveness, client satisfaction, cost-efficiency and cost-savings. Such an evaluation effort is becoming increasingly important due to the recent trend of organizational downsizing and coping with resource constraints. As Universities downsize, it can be challenging to justify allocating scarce funds to employee assistance programming. This study also includes review of EAPs among eighteen universities and colleges from around the country in order to compare program performance of the Pitt FSAP system.     

Abused Elders

No abstract available for this article.