Predicting New Graduate Nurses' Retention during Transition Using Decision Tree Methods: A Longitudinal Study.

Can a one-on-one mentorship program reduce the turnover rate of new graduate nurses in China? A longitudinal study

Transition experiences, intrapersonal resources, and job retention of new graduate nurses from accelerated and traditional nursing programs: A cross-sectional comparative study

Spatial distribution of natural disaster risk has been taking as a reference in urban planning and disaster management. Especially to the remote mountainous areas located in southwestern China where landslide disaster could cause enormous damage. In those areas, only few exposure researches could reveal disaster risk and exposure distribution which related to landslide occurrence since lack of landslide indicator information. This research aimed at combining landslide risk mapping with highly exposure mapping to reveal distribution of highly landslide exposure in a remote mountainous area: Panzhihua basin, Yangtze River, China. In this research, 203 landslide point data (103 occurrences and 100 absences) were collected based on governmental landslide inventory and fieldwork to build CART (Classification and Regression Tree) model, which was applied to investigate the relationship between landslide disaster and four independent variables (vegetation, elevation, slope and top soil type). Finally, we developed a landslide-prone hazard map based on transportation, threatened population and property distribution to illustrate potential socioeconomic damage of landslide occurrence. The output maps show that landslide-prone area basically follows valley stretch and Yangtze Rivers. Northeastern and center zone in study area are classified with higher risk to generate landslide disaster. Threaten population and property exposure shows similar distribution in northeastern and northwestern areas while detached in center and southeastern areas. To the transportation sector, there are 75.3km of local road, 8.6km of main road, 29.5km of railway and 39.2km of national highway are highly exposed to landslide occurrence respectively.

To develop a predictive system for the occurrence of nosocomial pneumonia in patients who had cardiac surgery performed. Retrospective cohort study.Setting. Two cardiologic tertiary care hospitals in Rio de Janeiro, Brazil. Between June 2000 and August 2002, there were 1,158 consecutive patients who had complex heart surgery performed. Patients older than 18 years who survived the first 48 postoperative hours were included in the study. The occurrence of pneumonia was diagnosed through active surveillance by an infectious diseases specialist according to the following criteria: the presence of new infiltrate on a radiograph in association with purulent sputum and either fever or leukocytosis until day 10 after cardiac surgery. Predictive models were built on the basis of logistic regression analysis and classification and regression tree (CART) analysis. The original data set was divided randomly into 2 parts, one used to construct the models (ie, "test sample") and the other used for validation (ie, "validation sample"). The area under the receiver-operating characteristic (ROC) curve was 69% for the logistic regression model and 76% for the CART model. Considering a probability greater than 7% to be predictive of pneumonia for both models, sensitivity was higher for the logistic regression models, compared with the CART models (64% vs 56%). However, the CART models had a higher specificity (92% vs 70%) and global accuracy (90% vs 70%) than the logistic regression models. Both models showed good performance, based on the 2-graph ROC, considering that 84.6% and 84.3% of the predictions obtained by regression and CART analyses were regarded as valid. Although our findings are preliminary, the predictive models we created showed fairly good specificity and fair sensitivity.

Research illustrates a clear gap between academic and practice facilities concerning new graduate nurses. Nursing programs are charged with training nursing graduates who can provide safe, quality patient care. One way to enhance the quality of patient care and aid with retention of new graduate nurses in health care facilities is through academic-practice partnerships. This article describes the planning, implementation, and evaluation of a partnership between a health care facility and an associate of applied science nursing degree program to aid with registered nurse retention and quality patient care.

BackgroundTo develop and validate a contrast-enhanced CT based classification tree model for classifying solid lung tumors in clinical patients into malignant or benign.MethodsBetween January 2015 and October 2017, 827 pathologically confirmed solid lung tumors (487 malignant, 340 benign; median size, 27.0 mm, IQR 18.0–39.0 mm) from 827 patients from a dedicated Chinese cancer hospital were identified. Nodules were divided randomly into two groups, a training group (575 cases) and a testing group (252 cases). CT characteristics were collected by two radiologists, and analyzed using a classification and regression tree (CART) model. For validation, we used the decision analysis threshold to evaluate the classification performance of the CART model and radiologist’s diagnosis (benign; malignant) in the testing group.ResultsThree out of 19 characteristics [margin (smooth; slightly lobulated/lobulated/spiculated), and shape (round/oval; irregular), subjective enhancement (no/uniform enhancement; heterogeneous enhancement)] were automatically generated by the CART model for classifying solid lung tumors. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy of the CART model is 98.5%, 58.1%, 80.6%, 98.6%, 79.8%, and 90.4%, 54.7%, 82.4% 98.5%, 74.2% for the radiologist’s diagnosis by using three-threshold decision analysis.ConclusionsTumor margin and shape, and subjective tumor enhancement were the most important CT characteristics in the CART model for classifying solid lung tumors as malignant. The CART model had higher discriminatory power than radiologist’s diagnosis. The CART model could help radiologists making recommendations regarding follow-up or surgery in clinical patients with a solid lung tumor.

False Financial Statements (FFS) have long been a serious problem in China and other Asian countries, which significantly dampen the confidence of the investors. Regardless of listed companies or non-listed companies, the percentage of financial statements that contained false information is quite high, which is one of the major reasons why China stock markets moved in the opposite direction towards its wonderful economic growth over the past few years. The objective of this research is to introduce one statistical technique — Classification and Regression Tree (CART), to identify and predict the impacts of FFS. We survey financial statements manipulation tricks, FFS indicators and FFS detection techniques from both China and international perspective, and further look into ten listed companies with known FFS history in China; combining these findings, we propose key indicators to work with CART. Our analysis includes 24 false financial reports, and 124 non-false financial reports. We use CART to develop two FFS detecting models: CART without industry benchmark and CART with industry benchmark. For supporting comparison, we also build a Logit regression which is a commonly used technique in FFS detecting. We find that CART is effective in distinguishing FFS from non-FFS. Both CART models achieve better accuracy in identifying fraud cases and making predictions than Logit regression does, and CART with industry benchmark is slightly better than CART without benchmark, but it does not always have superior performance. Our CART model also tries to capture the indicators and their combinations that could reflect firms with high possibility of FFS in China.

The professional learning community is a strategy to help staff development educators address recruitment and retention and organizational culture. The purpose of this article is to analyze the concept of learning communities and discuss their potential to increase the recruitment and retention of new graduate nurses and returning practitioners to the nursing profession. Incorporating learning communities into the healthcare organization will decrease the number of nurses leaving nursing, foster retention, and thus, enhance recruitment.

Faced with a threatened shortage of highly skilled, acute care pediatric nurses, an RN Internship in Pediatrics program for new graduates was brought from vision to reality. Goals of the program were to: 1) facilitate transition of the new graduate nurse to professional registered nurse (RN); 2) prepare a beginning level staff nurse who is confident and who provides competent and safe patient care; and 3) increase the commitment and retention of new graduate nurses within the organization. A 1-year pilot program evaluation demonstrated that the interns who had an average of 8 months of RN experience were comparable or better on all measures than were control group participants who obtained up to 2 years of RN experience. A return on investment of 67.3% was established.

Shallow landslides through land degrading not only lead to threat the properly and life of human but they also may produce huge ecosystem damages. The aim of this study was to compare the performance of two decision tree machine learning algorithms including classification and regression tree (CART) and reduced error pruning tree (REPTree) for shallow landslide susceptibility mapping in Bijar, Kurdistan province, Iran. We first used 20 conditioning factors and then they were tested by information gain ratio (IGR) technique to select the most important ones. We then constructed a geodatabase based on the selected factors along with a total of 111 landslide locations with a ratio of 80/20 (for calibration/validation). The performance of the models was checked by the true positive rate (TP Rate), false positive rate (FP Rate), precision, recall, F1-Measure, Kappa, mean absolute error, and area under the receiver operatic curve (AUC). Results of IGR specified that the slope angle and TWI had the most contribution to shallow landslide occurrence in the study area. Moreover, results concluded that although these models had a high goodness-of-fit and prediction accuracy, the CART model (AUC=0.856) outperformed the REPTree model (AUC=0.837). Therefore, the CART model can be used as a promising tool and also as a base classifier to hybrid with optimization algorithms and Meta classifiers for spatial prediction of shallow landslide-prone areas.

River suspended sediment modelling using the CART model: A comparative study of machine learning techniques

PurposePrior studies include auditor‐initiated (AI) and client‐initiated (CI) auditor changes together and conduct research as though they were the same. This paper aims to hypothesize that AI and CI changes are driven by different interests and to examine factors that could explain and predict them.Design/methodology/approachBased on the prior literature, the authors classified factors into AI and CI categories a priori, and then developed hypotheses that aligned with the classification. The data source is AuditAnalytics.FindingsThe univariate and multivariate analyses support the hypothesis that CI and AI separations are driven by different interests. The empirically determined prediction model (a classification and regression trees (CART)‐generated six‐step process), which performed better than other multivariate models, classifies AI and CI auditor separations with an accuracy rate of 68 percent.Research limitations/implicationsThe findings suggest that future research should incorporate the differences between AI and CI separations into their studies. One intriguing issue is the lack of importance of the variable audit fees to total fees in the CART model. Though statistically significant in some of the multivariate analyses, the variable is not important in the CART model. This issue merits further investigation as it could provide important insight into dependency issues that are presumed by current audit regulation.Practical implicationsThe paper has important implications for practice. It offers insight into factors that drive CI and AI auditor separations. The CART model may serve as a decision aid for both auditors and regulators as they contemplate and evaluate auditor change decisions.Originality/valueThe paper provides original insight into AI and CI auditor changes when considered as distinct issues that are driven by different interests.

Objective: To investigate the potential of Classification and Regression Trees (CARTs) for the diagnosis of thyroid lesions based on cell block immunocytochemistry and cytological outcome. Study Design: A total of 956 histologically confirmed cases (673 benign and 283 malignant) from patients with thyroid nodules were prepared via liquid-based cytology and evaluated; 4 additional slides were stained for cytokeratin 19 (CK-19), galectin 3 (Gal-3), Hector Battifora mesothelial cell 1 (HBME-1), and thyroglobulin. On the basis of immunocytochemistry and the cytological diagnosis, a CART algorithm was constructed and used for evaluation. Results: The major important factors contributing to the diagnostic CART model were: cytological outcome, CK-19, Gal-3, and HBME-1. The sensitivity and specificity of the cytological diagnosis were 96.27% and 88.26%, respectively (cut-off: category 3 of The Bethesda System [TBS-3]). The introduction of immunocytochemistry and the CART model increased the sensitivity and specificity to 98.88% and 99.11%, respectively. CK-19 presented the best performance for discriminating papillary thyroid carcinomas, followed by HBME-1 and Gal-3. In the TBS-2 cases, CK-19 and, subsequently, Gal-3 were important immunocytochemistry markers. Ultimately, CK-19 and HBME-1 on TBS-5 or TBS-6 cases demonstrated the best results. Conclusions: The hierarchical structure of the CART model provides a diagnostic algorithm linked with the risk of malignancy at every step of the procedure. It also provides guidance on the use of ancillary examinations as it goes by simple, human understandable rules.

Optimized modelling of countrywide soil organic carbon levels via an interpretable decision tree

Discriminative Ability of Heel Quantitative Ultrasound in Postmenopausal Women With Prevalent Low-Trauma Fractures: Application of Optimal Threshold Cutoff Values Using CART Models