The general objective of this study was to develop a substantive theory of how good nurses in acute care tune in to patient preferences in their nursing decision-making, and what knowledge they deploy in doing so.
Objective:Acknowledging study limitations in a scientific publication is a crucial element in scientific transparency and progress. However, limitation reporting is often inadequate. Natural language processing (NLP) methods could support automated reporting checks, improving research transparency. In this study, our objective was to develop a dataset and NLP methods to detect and categorize self-acknowledged limitations (e.g., sample size, blinding) reported in randomized controlled trial (RCT) publications.Methods:We created a data model of limitation types in RCT studies and annotated a corpus of 200 full-text RCT publications using this data model. We fine-tuned BERT-based sentence classification models to recognize the limitation sentences and their types. To address the small size of the annotated corpus, we experimented with data augmentation approaches, including Easy Data Augmentation (EDA) and Prompt-Based Data Augmentation (PromDA). We applied the best-performing model to a set of about 12K RCT publications to characterize self-acknowledged limitations at larger scale.Results:Our data model consists of 15 categories and 24 sub-categories (e.g., Population and its sub-category DiagnosticCriteria). We annotated 1090 instances of limitation types in 952 sentences (4.8 limitation sentences and 5.5 limitation types per article). A fine-tuned PubMedBERT model for limitation sentence classification improved upon our earlier model by about 1.5 absolute percentage points in F1 score (0.821 vs. 0.8) with statistical significance (). Our best-performing limitation type classification model, PubMedBERT fine-tuning with PromDA (Output View), achieved an F1 score of 0.7, improving upon the vanilla PubMedBERT model by 2.7 percentage points, with statistical significance ().Conclusion:The model could support automated screening tools which can be used by journals to draw the authors’ attention to reporting issues. Automatic extraction of limitations from RCT publications could benefit peer review and evidence synthesis, and support advanced methods to search and aggregate the evidence from the clinical trial literature.
MULTIFILE
Bachelor students of Hotel Management School Maastricht, part of Zuyd University of Applied Sciences in the Netherlands, start their educational program with a semester of orientation on Hotel Operations in theory and practice. The teaching staff was curious about students’ perception of what they learn during their duty in the Teaching Hotel Château Bethlehem. Students were interviewed about the learning environment, the coaching and their learning outcomes. The interview findings gave insight in different unexpected and subconscious learning outcomes together with the conditions under which they occur during practice-based learning. Findings were presented to the teaching staff during a work conference. The entire team emphasised the value of the research method for fine-tuning students’ learning outcomes.
![Students’ perception of what they learn in Teaching Hotel Château Bethlehem [EuroChrie Conference (Cooperative Education and Research for Hospitality & Tourism Educators), Freiburg (D) 16-19 October 2013]](https://publinova-harvester-content-acc.s3.amazonaws.com/thumbnails/files/previews/pdf/20240409032602041203.f2d9c2e4-9091-488e-b5a5-8c74be3083f1-thumbnail-400x300.png)
