A Topic Modeling Analysis of Nursing Handoff Studies

2021 ◽  
Author(s):  
Insook Cho ◽  
Eun Man Kim
Keyword(s):  
Patient Safety ◽  
Topic Modeling ◽  
Future Studies ◽  
Modeling Analysis ◽  
Safety Outcomes ◽  
Key Topics ◽  
Nursing Handoff

This study used topic modeling to analyse key topics of nursing handoff research. Six topics were identified. The findings indicate that future studies should implement the standardization of handoff tools and the use of bedside handoff, and evaluate their effects on patient safety outcomes.

scholarly journals Exploring patient safety outcomes for people with learning disabilities in acute hospital settings: a scoping review

BMJ Open ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. e047102
Author(s):  
Gemma Louch ◽  
Abigail Albutt ◽  
Joanna Harlow-Trigg ◽  
Sally Moore ◽  
Kate Smyth ◽  
...  
Keyword(s):  
Learning Disabilities ◽  
Patient Safety ◽  
Adverse Events ◽  
Grey Literature ◽  
Good Practice ◽  
Hospital Setting ◽  
Eligibility Criteria ◽  
Infant Outcomes ◽  
Safety Outcomes

ObjectivesTo produce a narrative synthesis of published academic and grey literature focusing on patient safety outcomes for people with learning disabilities in an acute hospital setting.DesignScoping review with narrative synthesis.MethodsThe review followed the six stages of the Arksey and O’Malley framework. We searched four research databases from January 2000 to March 2021, in addition to handsearching and backwards searching using terms relating to our eligibility criteria—patient safety and adverse events, learning disability and hospital setting. Following stakeholder input, we searched grey literature databases and specific websites of known organisations until March 2020. Potentially relevant articles and grey literature materials were screened against the eligibility criteria. Findings were extracted and collated in data charting forms.Results45 academic articles and 33 grey literature materials were included, and we organised the findings around six concepts: (1) adverse events, patient safety and quality of care; (2) maternal and infant outcomes; (3) postoperative outcomes; (4) role of family and carers; (5) understanding needs in hospital and (6) supporting initiatives, recommendations and good practice examples. The findings suggest inequalities and inequities for a range of specific patient safety outcomes including adverse events, quality of care, maternal and infant outcomes and postoperative outcomes, in addition to potential protective factors, such as the roles of family and carers and the extent to which health professionals are able to understand the needs of people with learning disabilities.ConclusionPeople with learning disabilities appear to experience poorer patient safety outcomes in hospital. The involvement of family and carers, and understanding and effectively meeting the needs of people with learning disabilities may play a protective role. Promising interventions and examples of good practice exist, however many of these have not been implemented consistently and warrant further robust evaluation.

A topic modeling analysis of Korea’s T&I research trends in the 2010s

Babel ◽  
2021 ◽  
Author(s):  
Changsoo Lee
Keyword(s):  
Topic Modeling ◽  
Research Output ◽  
Research Trends ◽  
Korean Language ◽  
Modeling Analysis ◽  
Trends Over Time ◽  
New Research ◽  
Topic Structure ◽  
Manual Classification

Abstract The present study aims to demonstrate the relevance of topic modeling as a new research tool for analyzing research trends in the T&I field. Until now, most efforts to this end have relied on manual classification based on pre-established typologies. This method is time- and labor-consuming, prone to subjective biases, and limited in describing a vast amount of research output. As a key component of text mining, topic modeling offers an efficient way of summarizing topic structure and trends over time in a collection of documents while being able to describe the entire system without having to rely on sampling. As a case study, the present paper applies the technique to analyzing a collection of abstracts from four Korean Language T&I journals for the 2010s decade (from 2010 to 2019). The analysis proves the technique to be highly successful in uncovering hidden topical structure and trends in the abstract corpus. The results are discussed along with implications of the technique for the T&I field.

An Evaluation of Patient Safety Event Report Categories Using Unsupervised Topic Modeling

2015 ◽  
Vol 54 (04) ◽  
pp. 338-345 ◽  
Author(s):  
A. Fong ◽  
R. Ratwani
Keyword(s):  
Patient Safety ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Topic Modeling ◽  
Free Text ◽  
Event Data ◽  
Event Type ◽  
Modeling Approach ◽  
Safety Event

SummaryObjective: Patient safety event data repositories have the potential to dramatically improve safety if analyzed and leveraged appropriately. These safety event reports often consist of both structured data, such as general event type categories, and unstructured data, such as free text descriptions of the event. Analyzing these data, particularly the rich free text narratives, can be challenging, especially with tens of thousands of reports. To overcome the resource intensive manual review process of the free text descriptions, we demonstrate the effectiveness of using an unsupervised natural language processing approach.Methods: An unsupervised natural language processing technique, called topic modeling, was applied to a large repository of patient safety event data to identify topics, or themes, from the free text descriptions of the data. Entropy measures were used to evaluate and compare these topics to the general event type categories that were originally assigned by the event reporter.Results: Measures of entropy demonstrated that some topics generated from the un-supervised modeling approach aligned with the clinical general event type categories that were originally selected by the individual entering the report. Importantly, several new latent topics emerged that were not originally identified. The new topics provide additional insights into the patient safety event data that would not otherwise easily be detected.Conclusion: The topic modeling approach provides a method to identify topics or themes that may not be immediately apparent and has the potential to allow for automatic reclassification of events that are ambiguously classified by the event reporter.

Recognizing and Responding to the “Toxic” Work Environment: Worker Safety, Patient Safety, and Abuse/Neglect in Nursing Homes

2017 ◽  
Vol 27 (12) ◽  
pp. 1870-1881 ◽  
Author(s):  
Carolyn E. Z. Pickering ◽  
Katie Nurenberg ◽  
Lawrence Schiamberg
Keyword(s):  
Patient Safety ◽  
Nursing Homes ◽  
Work Environment ◽  
Worker Safety ◽  
Nursing Assistant ◽  
Telephone Interviews ◽  
Certified Nursing Assistant ◽  
Safety Outcomes ◽  
Multistep Model ◽  
Constant Comparative Analysis

This grounded theory study examined how the certified nursing assistant (CNA) understands and responds to bullying in the workplace. Constant comparative analysis was used to analyze data from in-depth telephone interviews with CNAs ( N = 22) who experienced bullying while employed in a nursing home. The result of the analysis is a multistep model describing CNA perceptions of how, over time, they recognized and responded to the “toxic” work environment. The strategies used in responding to the “toxic” environment affected their care provision and were attributed to the development of several resident and worker safety outcomes. The data suggest that the etiology of abuse and neglect in nursing homes may be better explained by institutional cultures rather than individual traits of CNAs. Findings highlight the relationship between worker and patient safety, and suggest worker safety outcomes may be an indicator of quality in nursing homes.

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