The purpose of the research was the development of a questionnaire that can measure the behaviour of groups of students (for instance departments' cohorts) in Personal Information Management (PIM). Variables for the questionnaire were derived from the international literature on PIM. The questionnaire has been tested out on 79 students (last year before graduation) from four different departments of the Academy of ICT&Media at The Hague University of Applied Sciences. The students' responses were checked on consistency, item non response, desirability bias and information value of the results. All these criteria indicated that the questionnaire is an adequate tool for the assessment of PIM at an institutional level. The results that have been found for the four departments have not yet been discussed with the managers of the Academy and those of the individual departments. [De hier gepubliceerde versie is het 'accepted paper' van het origineel dat is gepubliceerd op www.springerlink.com . De officiële publicatie kan worden gedownload op http://www.springerlink.com/content/n0h3k71u85024xnt/]
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To study the ways in which compounds can induce adverse effects, toxicologists have been constructing Adverse Outcome Pathways (AOPs). An AOP can be considered as a pragmatic tool to capture and visualize mechanisms underlying different types of toxicity inflicted by any kind of stressor, and describes the interactions between key entities that lead to the adverse outcome on multiple biological levels of organization. The construction or optimization of an AOP is a labor intensive process, which currently depends on the manual search, collection, reviewing and synthesis of available scientific literature. This process could however be largely facilitated using Natural Language Processing (NLP) to extract information contained in scientific literature in a systematic, objective, and rapid manner that would lead to greater accuracy and reproducibility. This would support researchers to invest their expertise in the substantive assessment of the AOPs by replacing the time spent on evidence gathering by a critical review of the data extracted by NLP. As case examples, we selected two frequent adversities observed in the liver: namely, cholestasis and steatosis denoting accumulation of bile and lipid, respectively. We used deep learning language models to recognize entities of interest in text and establish causal relationships between them. We demonstrate how an NLP pipeline combining Named Entity Recognition and a simple rules-based relationship extraction model helps screen compounds related to liver adversities in the literature, but also extract mechanistic information for how such adversities develop, from the molecular to the organismal level. Finally, we provide some perspectives opened by the recent progress in Large Language Models and how these could be used in the future. We propose this work brings two main contributions: 1) a proof-of-concept that NLP can support the extraction of information from text for modern toxicology and 2) a template open-source model for recognition of toxicological entities and extraction of their relationships. All resources are openly accessible via GitHub (https://github.com/ontox-project/en-tox).
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Objective: Product Information Leaflets (PILs) are an important source of information for patients on their medication, but may cause confusion and questions. Patients then may seek clarification, for instance from pharmacy technicians. The aim of this study was to explore which questions pharmacy technicians get about PIL-related issues, why and when, and how they handle such questions. Methods: an online survey in a panel of 785 Dutch pharmacy technicians. Key results: Net response rate was 37%. PIL-related questions frequently concerned drug actions, problems with use, side effects, intolerances and pregnancy and lactation. Patients who received generic alternatives instead of the branded product they had received previously, also came more often to pharmacy staff with PIL-related questions. The requested information could not always be found in the PIL itself, not even by the pharmacy technicians themselves. They mentioned that the PIL is not easy to read, understand or recall. Conclusions: Pharmacy staff is often approached by patients having difficulties in understanding PILs. Even pharmacy technicians find PILs difficult to read and often use other sources of information. PIL layout and contents should become more standardized and easier to read and understand.
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