scholarly journals National Divergences in Perinatal Palliative Care Guidelines and Training in Tertiary NICUs

2021 ◽  
Vol 9 ◽  
Author(s):  
Antonio Boan Pion ◽  
Julia Baenziger ◽  
Jean-Claude Fauchère ◽  
Deborah Gubler ◽  
Manya J. Hendriks
Keyword(s):  
Palliative Care ◽  
Regression Models ◽  
Overall Response Rate ◽  
Nationwide Survey ◽  
Linear Regression Models ◽  
Further Training ◽  
Care Guidelines ◽  
Perinatal Palliative Care ◽  
Training Services ◽  
And Training

Objectives: Despite established principles of perinatal palliative care (PnPC), implementation into practice has shown inconsistencies. The aim of this study was to assess PnPC services, examine healthcare professionals (HCPs) awareness and availability of PnPC guidelines, and describe HCPs satisfaction with PC and guidelines.Material and Methods: A nationwide survey was conducted in Swiss tertiary NICUs between April-November 2019. Data were examined by descriptive statistics and linear regression models.Results: Overall response rate was 54% (65% physicians; 49% nurses; 72% psychosocial staff). Half of professionals (50%) received education in PC during their medical/nursing school, whereas 36% indicated they obtained further training in PnPC at their center. PnPC guidelines were available in 4/9 centers, with 68% HCPs being aware of the guideline. Professionals who had access to a PnPC team (P = 0.001) or were part of the nursing (P = 0.003) or psychosocial staff (P = 0.001) were more likely aware of having a guideline. Twenty-eight percent indicated being satisfied with PC in their center. Professionals with guideline awareness (P = 0.025), further training (P = 0.001), and access to a PnPC team (P < 0.001) were more likely to be satisfied, whereas HCPs with a nursing background (P < 0.001) were more likely to be dissatisfied. A majority expressed the need for a PnPC guideline (80%) and further PC training (94%).Conclusion: This study reveals lacking PnPC guidelines and divergences regarding onsite opportunities for continued training across Swiss level III NICUs. Extending PnPC guidelines and training services to all centers can help bridge the barriers created by fragmented practice.

Baseline Ability Makes a Larger Contribution to Race Performance in High-School Sprinters Than Race Experience or Training Exposure

2016 ◽  
Vol 28 (4) ◽  
pp. 565-571 ◽  
Author(s):  
Kristine E. Lynch ◽  
Alun Thomas ◽  
Bryan Gibson
Keyword(s):  
High School ◽  
Athletic Performance ◽  
Regression Models ◽  
Linear Regression Models ◽  
High School Athletes ◽  
12Th Grade ◽  
And Gender ◽  
Race Performance ◽  
And Training ◽  
Dominant Influence

Purpose:There has long been a debate regarding the importance of talent versus training in athletic performance. In this study we sought to quantify their relative contributions to the race performance of high-school sprinters.Methods:Using race results from the athletic.net website, we identified high-school athletes who participated in at least one race in both 9th and 12th grade in the 100 m, 200 m or 400 m. Athletes with a record of racing before high school were excluded from the analyses. Using separate linear regression models for each event and gender, we analyzed the effect of baseline ability, race experience and training exposure on race time in the 12th grade.Results:35,909 athletes, running a total of 1,627,652 races, contributed to the final analyses. The proportion of variance (R2) in 12th grade race times accounted for by baseline ability ranged from 40% to 51% depending on the event, and was consistently higher for females than males. Race experience explained 3.6–4.4% of the variance and training exposure explained 0.8–1.7%.Conclusion:Although race experience and training exposure impact high-school sprinters’ performance, baseline ability is the dominant influence.

Palliative care physicians' beliefs toward hematology and the care of patients with hematologic diseases: Results of a nationwide survey.

2018 ◽  
Vol 36 (34_suppl) ◽  
pp. 95-95
Author(s):  
Wil Leonard Santivasi ◽  
Kelly Wu ◽  
Mark Robert Litzow ◽  
Thomas William LeBlanc ◽  
Jacob J Strand
Keyword(s):  
Palliative Care ◽  
Regression Models ◽  
Nationwide Survey ◽  
Correlated Data ◽  
Statistical Testing ◽  
Hematologic Disease ◽  
Hematopoietic Stem ◽  
Hematologic Diseases ◽  
Logistic Regression Models ◽  
Physician Contact

95 Background: Palliative care (PC) specialists provide supportive care for patients with hematologic diseases (HD). However, the degree of engagement by PC physicians may be limited by the views of both PC physicians & hematologists. Prior studies have surveyed hematologists to identify barriers to delivery of PC, however PC physician views are unclear. This study aimed to examine views of PC physicians toward hematology. Methods: A survey was mailed to a random sample of the AAHPM physician contact list in 2017. Items focused on perceptions of their understanding of HD, comfort providing care, opinions regarding PC & hospice involvement, & beliefs about hematologists. Anonymized responses were assessed on a Likert scale. Statistical testing was based on logistic regression models with generalized estimating equations to account for correlated data within respondents. Results: 538 of 1000 surveys were completed. 51.9% of respondents were male. Community (37.2%), academic (36.7%) & hospice (26.2%) physicians were represented. Respondents were likelier to believe they understand the trajectories of lymphoma & myeloma than leukemia or patients undergoing hematopoietic stem cell transplantation (HSCT) (p < 0.001). They were more comfortable discussing prognosis (p < 0.001) & managing symptoms (p < 0.001) in lymphoma & myeloma than leukemia & HSCT. They were likelier to believe that hematologists’ perceptions of PC physicians limit collaboration rather than their own views of hematologists (p < 0.001). 80.2% agreed that hospice referrals are not made early enough. Conclusions: PC physicians’ understanding of trajectories & comfort caring for patients varies by hematologic disease. They perceive that hematologists’ perceptions are a larger barrier than their own & hospice referrals are delayed. These results provide insights into opportunities for better collaboration with hematologists.

scholarly journals May Measurement Month 2018: an analysis of blood pressure screening results from China

2020 ◽  
Vol 22 (Supplement_H) ◽  
pp. H40-H42
Author(s):  
Xin Chen ◽  
Yan Li ◽  
Zhe Hu ◽  
Min Liu ◽  
Jing Yu ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Regression Models ◽  
Alcohol Intake ◽  
Nationwide Survey ◽  
Overweight And Obesity ◽  
Hypertension Management ◽  
Linear Regression Models ◽  
Study Results ◽  
Central Database ◽  
And Control

Abstract To further improve awareness, treatment, and control of hypertension, the May Measurement Month (MMM) campaign continued in 2018 in China. Study subjects were adults aged 18 years or more, ideally those who had not their blood pressure (BP) measured for at least a year. Blood pressure was measured three times consecutively with a 1-min interval in the sitting position, using automated BP monitors in 288 342 participants and transmitted to a central database by a smartphone app. Questionnaire data were collected with the same app. After imputation, the overall proportion of hypertension was 29.8%. Of those with hypertension, the rates of awareness, treatment, and control were 62.3%, 57.3%, and 35.9%, respectively. In analysis based on linear regression models, both systolic and diastolic BP were higher with cigarette smoking, alcohol intake, and overweight and obesity. Our study results suggest that hypertension management is improving in comparison with the data in MMM 2017 and the nationwide survey in 2012–15, and several known lifestyle factors are key to hypertension management.

scholarly journals Genomic Machine Learning Meta-regression: Insights on Associations of Study Features with Reported Model Performance

2022 ◽  
Author(s):  
Stephen V Faraone ◽  
Eric J Barnett ◽  
Daniel Onete ◽  
Asif Salekin
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Regression Models ◽  
Best Practice ◽  
Model Performance ◽  
Mixed Effects ◽  
Data Sets ◽  
Linear Regression Models ◽  
Best Practice Guidelines ◽  
And Training

Background: Many studies have been conducted with the goal of correctly predicting diagnostic status of a disorder using the combination of genetic data and machine learning. The methods of these studies often differ drastically. It is often hard to judge which components of a study led to better results and whether better reported results represent a true improvement or an uncorrected bias inflating performance. Methods: In this systematic review, we extracted information about the methods used and other differentiating features in genomic machine learning models. We used the extracted features in mixed-effects linear regression models predicting model performance. We tested for univariate and multivariate associations as well as interactions between features. Results: In univariate models the number of hyperparameter optimizations reported and data leakage due to feature selection were significantly associated with an increase in reported model performance. In our multivariate model, the number of hyperparameter optimizations, data leakage due to feature selection, and training size were significantly associated with an increase in reported model performance. The interaction between number of hyperparameter optimizations and training size as well as the interaction between data leakage due to optimization and training size were significantly associated reported model performance. Conclusions: Our results suggest that methods susceptible to data leakage are prevalent among genomic machine learning research, which may result in inflated reported performance. The interactions of these features with training size suggest that if data leakage susceptible methods continue to be used, modelling efforts using larger data sets may result in unexpectedly lower results compared to smaller data sets. Best practice guidelines that promote the avoidance and recognition of data leakage may help the field advance and avoid biased results.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

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