Background:The nurse work environment is theorized to influence the quality of nursing care, nurse job outcomes, and patient outcomes. Objective:The aim of this meta-analysis was to evaluate quantitatively the association of the work environment with job and health outcomes.Research Design: Relevant studies published through September 2018 were identified. Inclusion criteria were use of a nationally endorsed work environment measure and reporting of odds ratios (ORs) and 95% confidence intervals from regression models of 4 outcome classes: nurse job outcomes, safety and quality ratings, patient outcomes, and patient satisfaction. Pooled ORs and confidence intervals were estimated for each outcome using fixed or random effects models.Subjects: Of 308 articles reviewed, 40 met inclusion criteria. After excluding 23 due to sample overlap or too few observations to meta-analyze, a set of 17 articles, comprising 21 independent samples, was analyzed. Cumulatively, these articles reported data from 2677 hospitals, 141 nursing units, 165,024 nurses, and 1,368,420 patients, in 22 countries.Measures: Practice Environment Scale of the Nursing Work Index, a National Quality Forum nursing care performance standard.Results: Consistent, significant associations between the work environment and all outcome classes were identified. Better work environments were associated with lower odds of negative nurse outcomes (average OR of 0.71), poor safety or quality ratings (average OR of 0.65

Labour and delivery (L&D) registered nurses (RNs) are the frontline providers during labour. They provide continuous bedside care that is instrumental to labour progression and critical to achieving optimal birth outcomes (Association of Women's Health Obstetric & Neonatal Nurses, 2018). Childbirth is the most common reason for hospitalization, with 4 million women giving birth each year, comprising 1 in 9 hospitalizations (McDermott, Elixhauser, & Sun, 2017).

Patient adherence is a critical factor in health outcomes. We present a framework to extract adherence information from electronic health records, including both sentence-level information indicating general adherence information (full, partial, none, etc.) and span-level information providing additional information such as adherence type (medication or nonmedication), reasons and outcomes. We annotate and make publicly available a new corpus of 3,000 de-identified sentences, and discuss the language physicians use to document adherence information. We also explore models based on state-of-the-art transformers to automate both tasks.

This paper presents a new, extensible annotation scheme for offensive language data sets. The annotation scheme expands coverage beyond fairly straightforward cases of offensive language to address several cases of complex, implicit, and/or pragmaticallytriggered offensive language. We apply the annotation scheme to create a new Complex Offensive Language Data Set for English (COLD-EN). The primary purpose of this data set is to diagnose how well systems for automatic detection of abusive language are able to classify three types of complex offensive language: reclaimed slurs, offensive utterances containing pejorative adjectival nominalizations (and no slur terms), and utterances conveying offense through linguistic distancing.COLD offers a straightforward framework for error analysis. Our vision is that researchers will use this data set to diagnose the strengths and weaknesses of their offensive language detection systems. In this paper, we diagnose some strengths and weaknesses of a top-performing offensive language detection system by: a) using it to classify COLD, and b) investigating its performance on the 10 fine-grained categories supported by our annotation scheme. We evaluate the system's performance when trained on five different standard data sets for offensive language detection. Systems trained on different data sets have different strengths and weaknesses, with most performing poorly on the phenomena of reclaimed slurs and pejorative nominalizations. NOTE: This paper contains sensitive and offensive material. The offensive materials are part of a complex puzzle we wish to better understand; they appear in the form of lightly-censored slurs and degrading insults. We do not condone this type of language, nor does it reflect the attitudes or beliefs of the authors.

<p>Flight delays represent a significant issue to airline profits and passenger satisfaction. Many factors can lead to a flight being delayed and/or canceled. The study evaluates flight delays, cancellations, and incident data with the goal of visualizing which airports, airlines, and cities, to visualize or states are experiencing the highest number of flight disruptions relative to others. Databases are commonly used for data analysis to allow pattern recognition and large data distribution and organization. The study will mainly serve the purpose of flight data retrieval, the compilation of data and database design, and finally output data visualizations.</p>