BackgroundFour- or five-option multiple choice questions (MCQs) are the standard in health-science disciplines, both on certification-level examinations and on in-house developed tests. Previous research has shown, however, that few MCQs have three or four functioning distractors. The purpose of this study was to investigate non-functioning distractors in teacher-developed tests in one nursing program in an English-language university in Hong Kong.MethodsUsing item-analysis data, we assessed the proportion of non-functioning distractors on a sample of seven test papers administered to undergraduate nursing students. A total of 514 items were reviewed, including 2056 options (1542 distractors and 514 correct responses). Non-functioning options were defined as ones that were chosen by fewer than 5% of examinees and those with a positive option discrimination statistic.ResultsThe proportion of items containing 0, 1, 2, and 3 functioning distractors was 12.3%, 34.8%, 39.1%, and 13.8% respectively. Overall, items contained an average of 1.54 (SD = 0.88) functioning distractors. Only 52.2% (n = 805) of all distractors were functioning effectively and 10.2% (n = 158) had a choice frequency of 0. Items with more functioning distractors were more difficult and more discriminating.ConclusionThe low frequency of items with three functioning distractors in the four-option items in this study suggests that teachers have difficulty developing plausible distractors for most MCQs. Test items should consist of as many options as is feasible given the item content and the number of plausible distractors; in most cases this would be three. Item analysis results can be used to identify and remove non-functioning distractors from MCQs that have been used in previous tests.

Classical molecular dynamics (MD), classical Monte Carlo (MC), and combined quantum mechanical/molecular mechanical (QM/MM) MD simulations were carried out to investigate the hydration structure of the Ni(II) ion in water using a newly constructed 2-body potential and a function correcting for 3-body effects. A 6-coordinate hydration structure with a maximum probability of the Ni-O distance at 2.25, 2.21, and 2.14 Å was observed by the classical MD, classical MC, and QM/MM-MD simulation with 3-body corrections, respectively, while an 8-coordinate structure was observed by the classical MD and MC simulations using only 2-body pair potentials. The average structure parameters obtained by the Hertree-Fock level QM/MM-MD simulation are in agreement with the experimental values. The validity of the 3-body correction function is discussed on the basis of the results for the classical and QM/MM simulations. During the classical MD simulation, a water exchange reaction was observed for the 6-coordinate Ni(II) ion. The water exchange reaction proceeded via a 5-coordinate intermediate with the lifetime of ca. 2.5 ps. The observed dissociative mechanism of the water exchange reaction is in accordance with experimental evidence.

An ab intio two-body analytical potential function was constructed to describe Mn(II)−water interactions. Classical Monte Carlo (MC) and molecular dynamics (MD) simulations have been performed to study the hydration structure of Mn(II). The study was extended to a combined QM/MM-MD level in order to investigate the influence of higher (n-body) terms. The structure of the hydrated ion is discussed in terms of radial distribution functions, coordination numbers, and angular distributions. The results of the QM/MM-MD simulations have been found to be much closer to the experimental values, proving that many-body effects play an important role in the description of the hydrated Mn(II) ion.

Background: Countries around the world are facing extraordinary challenges in implementing various measures to slow down the spread of the novel coronavirus (COVID-19). Guided by international recommendations, Saudi Arabia has implemented a series of infection control measures after the detection of the first confirmed case in the country. However, in order for these measures to be effective, public attitudes and compliance must be conducive as perceived risk is strongly associated with health behaviors. The primary objective of this study is to assess Saudis’ attitudes towards COVID-19 preventive measures to guide future health communication content. Methods: Naïve Bayes machine learning model was used to run Arabic sentiment analysis of Twitter posts through the Natural Language Toolkit (NLTK) library in Python. Tweets containing hashtags pertaining to seven public health measures imposed by the government were collected and analyzed. Results: A total of 53,127 tweets were analyzed. All measures, except one, showed more positive tweets than negative. Measures that pertain to religious practices showed the most positive sentiment. Discussion: Saudi Twitter users showed support and positive attitudes towards the infection control measures to combat COVID-19. It is postulated that this conducive public response is reflective of the overarching, longstanding popular confidence in the government. Religious notions may also play a positive role in preparing believers at times of crises. Findings of this study broadened our understanding to develop proper public health messages and promote stronger compliance with control measures to control COVID-19.

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