A study of clinical questions posed by hospital clinicians.

OBJECTIVE: This article presents the results of complementary research studies on the behaviors of hospital clinicians in asking clinical questions and the relationship between asking of questions, outcome of information searches, and success in problem solving. METHODS: Triangulation in research methods--a combination of mailed questionnaires, interviews, and a randomized controlled study--was employed to provide complementary views of the research problems under study. RESULTS: The survey and interviews found that clinical problems (concerned mainly with therapy and equipment or technology) were expressed as statements rather than questions (average number of concepts = 1.7), that only slightly more than half (higher for doctors) of problems could be solved, and that the majority of clinical questions were not well formed. An educational workshop however improved clinicians' formulation of questions, but the use of structured prompting was found to improve building of hypotheses in the doctors' group without training. The workshop also improved satisfaction with the obtained information and success in problem solving. Nonetheless, for both the experimental and control groups, more structured and complete questions or statements did not mean higher success rates in problem solving or higher satisfaction with obtained information. CONCLUSION: The triangulation methods have gathered complementary evidence to reject the hypothesis that building well-structured clinical questions would mean higher satisfaction with obtained information and higher success in problem solving.     

Setting the Students’ Bearings and Balance in Appraisal

The author, a professor at the University of Toronto, touches briefly on the extensive and rich archival literature that supports the teaching of macroappraisal, but notes that this is not the only educational material she offers her students when teaching appraisal theory. She discusses the usefulness to archivists of literature from the fields of ethnography, organizational knowing, records in history, personal documentary behaviour, memory, and communications, noting that the use of texts from these fields can encourage students to reflect on their own presumptions and to develop a taste for the wide reading and research that must support appraisal.     

Research trends and collaborations by applied science researchers in South African universities of technology: 2007–2017

This paper analyses the publication patterns of researchers in the field of applied sciences at Universities of Technology in South Africa. Aspects investigated include publications in SCOPUS-listed journals; number of citations and countries of publication. Collaborative research patterns at national and international levels were also investigated. A bibliometric analysis approach was followed using SCOPUS as the main source of data and analysing the articles published in selected applied science disciplines. Results show that researchers in the field of applied sciences in universities of technology have increased their number of publications over the past 10?years and are also working in conjunction with other researchers both nationally and internationally. The analysis is an important addition to the field in South Africa which helps in measuring how institutions are positively responding to government incentives in research. The results are also important to information professionals who are increasingly playing an important role in research impact assessments.     

Narrative Persuasion in a New Media Environment: The Impact of Binge-Watching and Second-Screening

The study explored the role of second-screen use and binge-watching in moderating entertainment education (EE) effects. A pretest/posttest experiment of 273 viewers of East Los High measured the effects of exposure to three subplots, concerning alcohol abuse, abortion, and immigration. The effect of identification with characters on change in attitudes was significantly moderated by second-screen use and binge-watching. In particular, second-screening tended to enhance the positive impact of identification; whereas, among respondents who binge-watched the show, the effects of identification were weaker.     

Nullis in Verba: Advancing Research through Transparency and Openness

Nullis in verba was chosen as the motto of the Royal Society, as it embodied the culture of transparency and collaboration that were the hallmarks of the Royal Society and of the scientific method it promoted. Communication, using the tools of the day, was essential to this community of scientists as they created and shared new knowledge. Almost three hundred years later, libraries continue to advance principles relating to the importance of collaboration and transparency. This two-part article series, published jointly in International Information and Library Review and Technical Services Quarterly explores notions of transparency and collaboration in research and how these ideas are impacting the world of librarianship – from information creation, organization and access perspectives.     

Setting a Non-Agenda: Effects of a Perceived Lack of Problems in Recent News or Twitter

The mere perception that news has given certain problems more coverage can lead the audience to assume that those problems are more important. Given that the news media, at times, obsesses over relatively trivial matters, and given that the audience is increasingly able to filter media exposure, it is worth asking what happens when audience members perceive that recent media coverage has not emphasized any very important problems. In such cases, audience members might assume that any problems facing the nation must not be particularly important. We explicate this attitude of political complacency, test whether perceived media agendas lacking important problems can influence it, and explore whether complacency helps explain political disengagement. We also explore whether these effects generalize beyond news, to new media gatekeepers such as Twitter. Two experiments tested effects of a perceived absence of important problems in recent news or Twitter content. In the case of news, but not Twitter, this increased complacency in both studies. Study 2 added a no-exposure control and found that effects on complacency were driven by the cueing of nonproblem stories, not by the absence of problem story cues. Both studies validated complacency as a predictor of political disengagement.     

Using word embeddings in Twitter election classification

Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, as well as the parameters of the word embedding training process, namely the context window size, the dimensionality and the number of negative samples, on the attained classification performance. By comparing the classification results of word embedding models that have been trained using different background corpora (e.g. Wikipedia articles and Twitter microposts), we show that the background data should align with the Twitter classification dataset both in data type and time period to achieve significantly better performance compared to baselines such as SVM with TF-IDF. Moreover, by evaluating the results of word embedding models trained using various context window sizes and dimensionalities, we find that large context window and dimension sizes are preferable to improve the performance. However, the number of negative samples parameter does not significantly affect the performance of the CNN classifiers. Our experimental results also show that choosing the correct word embedding model for use with CNN leads to statistically significant improvements over various baselines such as random, SVM with TF-IDF and SVM with word embeddings. Finally, for out-of-vocabulary (OOV) words that are not available in the learned word embedding models, we show that a simple OOV strategy to randomly initialise the OOV words without any prior knowledge is sufficient to attain a good classification performance among the current OOV strategies (e.g. a random initialisation using statistics of the pre-trained word embedding models).