Regional Patterns of Access and Participation in Non-Formal Cultural Education in Germany

(1) Background: The equality of life chances in Germany is often assessed along the lines of a west/east and urban/rural differentiation in which the latter usually perform worse. One currently popular proposal for addressing these inequalities is to strengthen cultural and arts education. The question arises to what extent regional characteristics genuinely influence participation opportunities and to what extent individual resources still play a decisive role. (2) Methods: Using descriptive analyses and multilevel logistic regression modelling, we investigate the distribution of and participation in non-formal cultural education amongst German youth. (3) Results: We find that differences are more complex than a simple west/east or urban/rural divides. Rather, cultural activities must be considered in terms of their character in order to assess the mechanisms at play. There seem to be differences in the dependency on district funding between very peripheral and very central districts that frame the cultural infrastructure. (4) Conclusions: Regional discrepancies are not uniformly distributed across different fields of education or infrastructure. Simplifying statements that classify peripheral regions the general losers can be refuted here. Simultaneously, more comprehensive data could yield significantly more results than we are currently able to produce.

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Multilevel logistic regression modelling with correlated random effects: application to the Smoking Cessation for Youth study

Statistics in Medicine ◽

10.1002/sim.2472 ◽

2006 ◽

Vol 25 (22) ◽

pp. 3864-3876 ◽

Cited By ~ 11

Author(s):

Kui Wang ◽

Andy H. Lee ◽

Greg Hamilton ◽

Kelvin K. W. Yau

Keyword(s):

Smoking Cessation ◽

Logistic Regression ◽

Random Effects ◽

Multilevel Logistic Regression ◽

Regression Modelling ◽

Correlated Random Effects ◽

Youth Study ◽

Logistic Regression Modelling

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An improved medical admissions risk system using multivariable fractional polynomial logistic regression modelling

QJM ◽

10.1093/qjmed/hcp149 ◽

2009 ◽

Vol 103 (1) ◽

pp. 23-32 ◽

Cited By ~ 74

Author(s):

B. Silke ◽

J. Kellett ◽

T. Rooney ◽

K. Bennett ◽

D. O’Riordan

Keyword(s):

Logistic Regression ◽

Regression Modelling ◽

Fractional Polynomial ◽

Logistic Regression Modelling

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Exploring individual and demographic characteristics and their relation to CHNRI Criteria from an international public stakeholder group: an analysis using random intercept and logistic regression modelling

Journal of Global Health ◽

10.7189/jogh.09.010701 ◽

2019 ◽

Vol 9 (1) ◽

Author(s):

Kerri Wazny ◽

Niall Anderson ◽

Diego G Bassani ◽

John Ravenscroft ◽

Kit Yee Chan ◽

...

Keyword(s):

Logistic Regression ◽

Demographic Characteristics ◽

Stakeholder Group ◽

Regression Modelling ◽

Random Intercept ◽

Logistic Regression Modelling

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Reply on Interactive comment on “Incorporating multi-source remote sensing in the detection of earthquake-damaged buildings based on logistic regression modelling” by Qiang Li et al.

10.5194/nhess-2019-20-ac1 ◽

2019 ◽

Author(s):

Qiang Li

Keyword(s):

Remote Sensing ◽

Logistic Regression ◽

Regression Modelling ◽

Logistic Regression Modelling

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Machine-Learning vs. Expert-Opinion Driven Logistic Regression Modelling for Predicting 30-Day Unplanned Rehospitalisation in Preterm Babies: A Prospective, Population-Based Study (EPIPAGE 2)

Frontiers in Pediatrics ◽

10.3389/fped.2020.585868 ◽

2021 ◽

Vol 8 ◽

Author(s):

Robert A. Reed ◽

Andrei S. Morgan ◽

Jennifer Zeitlin ◽

Pierre-Henri Jarreau ◽

Héloïse Torchin ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forest ◽

Regression Model ◽

Expert Opinion ◽

Logistic Regression Model ◽

Population Based ◽

Regression Modelling ◽

Preterm Babies ◽

Logistic Regression Modelling

Introduction: Preterm babies are a vulnerable population that experience significant short and long-term morbidity. Rehospitalisations constitute an important, potentially modifiable adverse event in this population. Improving the ability of clinicians to identify those patients at the greatest risk of rehospitalisation has the potential to improve outcomes and reduce costs. Machine-learning algorithms can provide potentially advantageous methods of prediction compared to conventional approaches like logistic regression.Objective: To compare two machine-learning methods (least absolute shrinkage and selection operator (LASSO) and random forest) to expert-opinion driven logistic regression modelling for predicting unplanned rehospitalisation within 30 days in a large French cohort of preterm babies.Design, Setting and Participants: This study used data derived exclusively from the population-based prospective cohort study of French preterm babies, EPIPAGE 2. Only those babies discharged home alive and whose parents completed the 1-year survey were eligible for inclusion in our study. All predictive models used a binary outcome, denoting a baby's status for an unplanned rehospitalisation within 30 days of discharge. Predictors included those quantifying clinical, treatment, maternal and socio-demographic factors. The predictive abilities of models constructed using LASSO and random forest algorithms were compared with a traditional logistic regression model. The logistic regression model comprised 10 predictors, selected by expert clinicians, while the LASSO and random forest included 75 predictors. Performance measures were derived using 10-fold cross-validation. Performance was quantified using area under the receiver operator characteristic curve, sensitivity, specificity, Tjur's coefficient of determination and calibration measures.Results: The rate of 30-day unplanned rehospitalisation in the eligible population used to construct the models was 9.1% (95% CI 8.2–10.1) (350/3,841). The random forest model demonstrated both an improved AUROC (0.65; 95% CI 0.59–0.7; p = 0.03) and specificity vs. logistic regression (AUROC 0.57; 95% CI 0.51–0.62, p = 0.04). The LASSO performed similarly (AUROC 0.59; 95% CI 0.53–0.65; p = 0.68) to logistic regression.Conclusions: Compared to an expert-specified logistic regression model, random forest offered improved prediction of 30-day unplanned rehospitalisation in preterm babies. However, all models offered relatively low levels of predictive ability, regardless of modelling method.

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