Zoekresultaten

Exploring automatic text‑to‑sign translation in a healthcare setting

Communication between healthcare professionals and deaf patients has been particularly challenging during the COVID-19 pandemic. We have explored the possibility to automatically translate phrases that are frequently used in the diagnosis and treatment of hospital patients, in particular phrases related to COVID-19, from Dutch or English to Dutch Sign Language (NGT). The prototype system we developed displays translations either by means of pre-recorded videos featuring a deaf human signer (for a limited number of sentences) or by means of animations featuring a computer-generated signing avatar (for a larger, though still restricted number of sentences). We evaluated the comprehensibility of the signing avatar, as compared to the human signer. We found that, while individual signs are recognized correctly when signed by the avatar almost as frequently as when signed by a human, sentence comprehension rates and clarity scores for the avatar are substantially lower than for the human signer. We identify a number of concrete limitations of the JASigning avatar engine that underlies our system. Namely, the engine currently does not offer sufficient control over mouth shapes, the relative speed and intensity of signs in a sentence (prosody), and transitions between signs. These limitations need to be overcome in future work for the engine to become usable in practice.

Corporate Reputation of Companies on Twitter Seen from a Sustainability Perspective

Corporate reputation is becoming increasingly important for firms; social media platforms such as Twitter are used to convey their message. In this paper, corporate reputation will be assessed from a sustainability perspective. Using sentiment analysis, the top 100 brands of the Netherlands were scraped and analyzed. The companies were registered in the sustainable industry classification system (SICS) to perform the analysis on an industry level. A semantic search tool called Open Semantic Desktop Search was used to filter through the data to find keywords related to sustainability and corporate reputation. Findings show that companies that tweet more often about corporate reputation and sustainability receive overall a more positive sentiment from the public.

Automatic recognition of self-acknowledged limitations in clinical research literature

Objective: To automatically recognize self-acknowledged limitations in clinical research publications to support efforts in improving research transparency.Methods: To develop our recognition methods, we used a set of 8431 sentences from 1197 PubMed Central articles. A subset of these sentences was manually annotated for training/testing, and inter-annotator agreement was calculated. We cast the recognition problem as a binary classification task, in which we determine whether a given sentence from a publication discusses self-acknowledged limitations or not. We experimented with three methods: a rule-based approach based on document structure, supervised machine learning, and a semi-supervised method that uses self-training to expand the training set in order to improve classification performance. The machine learning algorithms used were logistic regression (LR) and support vector machines (SVM).Results: Annotators had good agreement in labeling limitation sentences (Krippendorff's α = 0.781). Of the three methods used, the rule-based method yielded the best performance with 91.5% accuracy (95% CI [90.1-92.9]), while self-training with SVM led to a small improvement over fully supervised learning (89.9%, 95% CI [88.4-91.4] vs 89.6%, 95% CI [88.1-91.1]).Conclusions: The approach presented can be incorporated into the workflows of stakeholders focusing on research transparency to improve reporting of limitations in clinical studies.

Causes of reporting bias: a theoretical framework [version 2; peer review: 2 approved]

Reporting of research findings is often selective. This threatens the validity of the published body of knowledge if the decision to report depends on the nature of the results. The evidence derived from studies on causes and mechanisms underlying selective reporting may help to avoid or reduce reporting bias. Such research should be guided by a theoretical framework of possible causal pathways that lead to reporting bias. We build upon a classification of determinants of selective reporting that we recently developed in a systematic review of the topic. The resulting theoretical framework features four clusters of causes. There are two clusters of necessary causes: (A) motivations (e.g. a preference for particular findings) and (B) means (e.g. a flexible study design). These two combined represent a sufficient cause for reporting bias to occur. The framework also features two clusters of component causes: (C) conflicts and balancing of interests referring to the individual or the team, and (D) pressures from science and society. The component causes may modify the effect of the necessary causes or may lead to reporting bias mediated through the necessary causes. Our theoretical framework is meant to inspire further research and to create awareness among researchers and end-users of research about reporting bias and its causes.

Cluttering identified: differential diagnostics between cluttering, stuttering and speech impairment related to learning difficulties

Learning about racism: A week-by-week qualitative exploration of two white Dutch primary student teachers’ emotional responses during a Critical Race Theory based course

This qualitative study explored emotional responses of two white Dutch student teachers during a Critical Race Theory (CRT) based course. Following Plutchik's (2001) classification of 32 emotions, the analysis of their weekly diaries resulted in the identification of 16 emotions. In both diaries similar emotional responses were identified. However, the analysis did not reveal a straightforward path these students emotionally went through. The number and types of emotional responses, both comfortable and uncomfortable, fluctuated weekly and occurred simultaneously in various combinations. Even when similar emotional responses were identified, students connected differently to the course content. This could be explained by different starting points both students had when entering the course. The findings add to past work by identifying a variety and complexity of emotional responses of white student teachers during a CRT based course and can be used to create course conditions to prepare teachers for contributing to anti-racist education.