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.
AIM: To identify what determinants influence the prevalence and accuracy of nursing diagnosis documentation in clinical practice.BACKGROUND: Nursing diagnoses guide and direct nursing care. They are the foundation for goal setting and provide the basis for interventions. The literature mentions several factors that influences nurses' documentation of diagnoses, such as a nurse's level of education, patient's condition and the ward environment.DESIGN: Systematic review.METHOD: MEDLINE and CINAHL databases were searched using the following headings and keywords: nursing diagnosis, nursing documentation, hospitals, influence, utilisation, quality, implementation and accuracy. The search was limited to articles published between 1995-October 2009. Studies were only selected if they were written in English and were primary studies addressing factors that influence nursing diagnosis documentation.RESULTS: In total, 24 studies were included. Four domains of factors that influence the prevalence and accuracy of diagnoses documentation were found: (1) the nurse as a diagnostician, (2) diagnostic education and resources, (3) complexity of a patient's situation and (4) hospital policy and environment.CONCLUSION: General factors, which influence decision-making, and nursing documentation and specific factors, which influence the prevalence and accuracy of nursing diagnoses documentation, need to be distinguished. To support nurses in documenting their diagnoses accurately, we recommend taking a comprehensive perspective on factors that influence diagnoses documentation. A conceptual model of determinants that influence nursing diagnoses documentation, as presented in this study, may be helpful as a reference for nurse managers and nurse educators.RELEVANCE TO CLINICAL PRACTICE: This review gives hospital management an overview of determinants for possible quality improvements in nursing diagnoses documentation that needs to be undertaken in clinical practice.
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Abstract: Existing frailty models have enhanced research and practice; however, none of the models accounts for the perspective of older adults upon defining and operationalizing frailty. We aim to propose a mixed conceptual model that builds on the integral model while accounting for older adults’ perceptions and lived experiences of frailty. We conducted a traditional literature review to address frailty attributes, risk factors, consequences, perceptions, and lived experiences of older adults with frailty. Frailty attributes are vulnerability/susceptibility, aging, dynamic, complex, physical, psychological, and social. Frailty perceptions and lived experience themes/subthemes are refusing frailty labeling, being labeled “by others” as compared to “self-labeling”, from the perception of being frail towards acting as being frail, positive self-image, skepticism about frailty screening, communicating the term “frail”, and negative and positive impacts and experiences of frailty. Frailty risk factors are classified into socio-demographic, biological, physical, psychological/cognitive, behavioral, and situational/environmental factors. The consequences of frailty affect the individual, the caregiver/family, the healthcare sector, and society. The mixed conceptual model of frailty consists of interacting risk factors, interacting attributes surrounded by the older adult’s perception and lived experience, and interacting consequences at multiple levels. The mixed conceptual model provides a lens to qualify frailty in addition to quantifying it.
The main objective is to write a scientific paper in a peer-reviewed Open Access journal on the results of our feasibility study on increasing physical activity in home dwelling adults with chronic stroke. We feel this is important as this article aims to close a gap in the existing literature on behavioral interventions in physical therapy practice. Though our main target audience are other researchers, we feel clinical practice and current education on patients with stroke will benefit as well.
