An analysis of patient motivation for seeking online treatment for binge eating disorder-A mixed methods study combining systematic text condensation with sentiment analysis.

Objective: Online treatment for binge eating disorder (BED) is an easily available option for treatment compared to most standard treatment procedures. However, little is known about how motivation types characterize this population and how these impact treatment adherence and effect in an online setting. Therefore, we aimed to investigate a sample of written motivation statements from BED patients, to learn more about how treatment and online treatment in particular, presents in this population. Methods: Using self-determination theory in a mixed methods context, we investigated which types of motivation were prevalent in our sample, how this was connected with patient sentiment, and how these constructs influence treatment and adherence. Results: Contrary to what most current literature suggests, we found that in our sample (n = 148), motivation type was not connected with treatment outcome. We did find a strong association between sentiment scores and motivation types, indicating the model is apt at detecting effects. We found that when comparing an adult and young adult population, they did not differ in motivation type and the treatment was equally effective in young adults and adults. In the sentiment scores there was a difference between sentiment score and adherence in the young adult group, as the more positive the young adults were, the less likely they were to complete the program. Discussion: Because motivation type does not influence online treatment to the same degree as it would in face-to-face treatment it indicates that the typical barriers to treatment may be less crucial in an online setting. This should be considered during intake; as less motivated patients may be able to adhere better to online treatment, because the latter imposes fewer barriers of the kind that only strong motivation can overcome. The fact that motivation type and sentiment score of the written texts are strongly associated, indicate a potential for automated models to detect motivation based on sentiment.

Words by the tail: Assessing lexical diversity in scholarly titles using frequency-rank distribution tail fits.

This research assesses the evolution of lexical diversity in scholarly titles using a new indicator based on zipfian frequency-rank distribution tail fits. At the operational level, while both head and tail fits of zipfian word distributions are more independent of corpus size than other lexical diversity indicators, the latter however neatly outperforms the former in that regard. This benchmark-setting performance of zipfian distribution tails proves extremely handy in distinguishing actual patterns in lexical diversity from the statistical noise generated by other indicators due to corpus size fluctuations. From an empirical perspective, analysis of Web of Science (WoS) article titles from 1975 to 2014 shows that the lexical concentration of scholarly titles in Natural Sciences & Engineering (NSE) and Social Sciences & Humanities (SSH) articles increases by a little less than 8% over the whole period. With the exception of the lexically concentrated Mathematics, Earth & Space, and Physics, NSE article titles all increased in lexical concentration, suggesting a probable convergence of concentration levels in the near future. As regards to SSH disciplines, aggregation effects observed at the disciplinary group level suggests that, behind the stable concentration levels of SSH disciplines, a cross-disciplinary homogenization of the highest word frequency ranks may be at work. Overall, these trends suggest a progressive standardization of title wording in scientific article titles, as article titles get written using an increasingly restricted and cross-disciplinary set of words.

Beyond funding: Acknowledgement patterns in biomedical, natural and social sciences.

For the past 50 years, acknowledgments have been studied as important paratextual traces of research practices, collaboration, and infrastructure in science. Since 2008, funding acknowledgments have been indexed by Web of Science, supporting large-scale analyses of research funding. Applying advanced linguistic methods as well as Correspondence Analysis to more than one million acknowledgments from research articles and reviews published in 2015, this paper aims to go beyond funding disclosure and study the main types of contributions found in acknowledgments on a large scale and through disciplinary comparisons. Our analysis shows that technical support is more frequently acknowledged by scholars in Chemistry, Physics and Engineering. Earth and Space, Professional Fields, and Social Sciences are more likely to acknowledge contributions from colleagues, editors, and reviewers, while Biology acknowledgments put more emphasis on logistics and fieldwork-related tasks. Conflicts of interest disclosures (or lack of thereof) are more frequently found in acknowledgments from Clinical Medicine, Health and, to a lesser extent, Psychology. These results demonstrate that acknowledgment practices truly do vary across disciplines and that this can lead to important further research beyond the sole interest in funding.