Wirtschaftliche und soziale Determinanten der Arbeitszeitpolitik : zur Geschichte des Kampfes um die Verkürzung der Arbeitszeit

"Der Aufsatz stellt die Entwicklung der Arbeitszeit sowie die sozialpolitischen Auseinandersetzungen um die Arbeitszeitfrage in Deutschland in der Zeit vor dem ersten Weltkrieg, in der Weimarer Republik und in den fünfziger und sechziger Jahren in der Bundesrepublik dar. Die Entwicklung der Tages- und Wochenarbeitszeit wird sowohl unter dem Gesichtspunkt ihrer allgemeinen Veränderung als auch ihrer Differenzierung nach Branchen und Beschäftigungsgruppen skizziert, wobei das lückenhafte statistische Material allerdings häufig nur annähernde Angaben zuläßt. Darüber hinaus wird die Entstehung von Urlaubsregelungen behandelt. ... Die Auseinandersetzungen um die Arbeitszeitfrage werden von ihren wirtschaftlichen und sozialen Hintergründen in den einzelnen historischen Perioden her dargestellt. Für die Zeit vor dem ersten Weltkrieg wird dabei insbesondere auf die Bedeutung der Arbeitszeitfrage für die Entfaltung der Gewerkschaftsbewegung und auf die Zusammenhänge zwischen Qualifikation, Mechanisierung und Arbeitszeitverkürzung eingegangen. In der Darstellung der Auseinandersetzungen um den Acht-Stunden-Tag während der Weimarer Republik wird versucht, die Ursachen der Politisierung der Arbeitszeitfrage in dieser Periode herauszuarbeiten. Im Gegensatz dazu führten die tariflichen Arbeitszeitverkürzungen in den fünfziger und sechziger Jahren in der Bundesrepublik kaum zu größeren Konflikten, was von den wirtschaftlichen Rahmenbedingungen und der spezifischen sozialen Interessenlage der Arbeitnehmer in dieser Zeit her erklärt werden kann." (Autorenreferat)Arbeitszeitpolitik - Determinanten, Arbeitszeitverkürzung, Arbeitszeitentwicklung, Arbeitszeit - historische Entwicklung, Deutsches Reich, Bundesrepublik Deutschland

Influence of motivation, self-efficacy and situational factors on the teaching quality of clinical educators

Abstract Background Being exposed to good teachers has been shown to enhance students’ knowledge and their clinical performance, but little is known about the underlying psychological mechanisms that provide the basis for being an excellent medical teacher. Self-Determination Theory (SDT) postulates that more self-regulated types of motivation are associated with higher performance. Social Cognitive Theory (SCT) focuses on self-efficacy that has been shown to be positively associated with performance. To investigate the influences of different types of teaching motivation, teaching self-efficacy, and teachers’ perceptions of students’ skills, competencies and motivation on teaching quality. Methods Before the winter semester 2014, physicians involved in bedside teaching in internal medicine at the University Medical Center Hamburg-Eppendorf completed a questionnaire with sociodemographic items and instruments measuring different dimensions of teaching motivation as well as teaching self-efficacy. During the semester, physicians rated their perceptions of the participating students who rated the teaching quality after each lesson. We performed a random intercept mixed-effects linear regression with students’ ratings of teaching quality as the dependent variable and students’ general interest in a subject as covariate. We explored potential associations between teachers’ dispositions and their perceptions of students’ competencies in a mixed-effects random intercept logistic regression. Results 94 lessons given by 55 teachers with 500 student ratings were analyzed. Neither teaching motivation nor teaching self-efficacy were directly associated with students’ rating of teaching quality. Teachers’ perceptions of students’ competencies and students’ general interest in the lesson’s subject were positively associated with students’ rating of teaching quality. Physicians’ perceptions of their students’ competencies were significantly positively predicted by their teaching self-efficacy. Conclusions Teaching quality might profit from teachers who are self-efficacious and able to detect their students’ competencies. Students’ general interest in a lesson’s subject needs to be taken into account when they are asked to evaluate teaching quality

Additional file 2: of Influence of motivation, self-efficacy and situational factors on the teaching quality of clinical educators

Questionnaire for the student ratings of teaching quality. Scales and translated items of the questionnaire for the student ratings of teaching quality including means and standard deviations, Cronbach’s alphas and factor loadings in the confirmatory factor analysis. (DOCX 23 kb

Additional file 1: of Influence of motivation, self-efficacy and situational factors on the teaching quality of clinical educators

Questionnaire for the situational variables as perceived by the physicians. English and original German items for measuring physicians’ perceptions of the situational variables. (DOCX 24 kb

Identification of Significant Features by the Global Mean Rank Test

<div><p>With the introduction of omics-technologies such as transcriptomics and proteomics, numerous methods for the reliable identification of significantly regulated features (genes, proteins, etc.) have been developed. Experimental practice requires these tests to successfully deal with conditions such as small numbers of replicates, missing values, non-normally distributed expression levels, and non-identical distributions of features. With the MeanRank test we aimed at developing a test that performs robustly under these conditions, while favorably scaling with the number of replicates. The test proposed here is a global one-sample location test, which is based on the mean ranks across replicates, and internally estimates and controls the false discovery rate. Furthermore, missing data is accounted for without the need of imputation. In extensive simulations comparing MeanRank to other frequently used methods, we found that it performs well with small and large numbers of replicates, feature dependent variance between replicates, and variable regulation across features on simulation data and a recent two-color microarray spike-in dataset. The tests were then used to identify significant changes in the phosphoproteomes of cancer cells induced by the kinase inhibitors <i>erlotinib</i> and 3-MB-PP1 in two independently published mass spectrometry-based studies. MeanRank outperformed the other global rank-based methods applied in this study. Compared to the popular Significance Analysis of Microarrays and Linear Models for Microarray methods, MeanRank performed similar or better. Furthermore, MeanRank exhibits more consistent behavior regarding the degree of regulation and is robust against the choice of preprocessing methods. MeanRank does not require any imputation of missing values, is easy to understand, and yields results that are easy to interpret. The software implementing the algorithm is freely available for academic and commercial use.</p></div

Pareto Optimization Identifies Diverse Set of Phosphorylation Signatures Predicting Response to Treatment with Dasatinib

<div><p>Multivariate biomarkers that can predict the effectiveness of targeted therapy in individual patients are highly desired. Previous biomarker discovery studies have largely focused on the identification of single biomarker signatures, aimed at maximizing prediction accuracy. Here, we present a different approach that identifies multiple biomarkers by simultaneously optimizing their predictive power, number of features, and proximity to the drug target in a protein-protein interaction network. To this end, we incorporated NSGA-II, a fast and elitist multi-objective optimization algorithm that is based on the principle of Pareto optimality, into the biomarker discovery workflow. The method was applied to quantitative phosphoproteome data of 19 non-small cell lung cancer (NSCLC) cell lines from a previous biomarker study. The algorithm successfully identified a total of 77 candidate biomarker signatures predicting response to treatment with dasatinib. Through filtering and similarity clustering, this set was trimmed to four final biomarker signatures, which then were validated on an independent set of breast cancer cell lines. All four candidates reached the same good prediction accuracy (83%) as the originally published biomarker. Although the newly discovered signatures were diverse in their composition and in their size, the central protein of the originally published signature — integrin β4 (ITGB4) — was also present in all four Pareto signatures, confirming its pivotal role in predicting dasatinib response in NSCLC cell lines. In summary, the method presented here allows for a robust and simultaneous identification of multiple multivariate biomarkers that are optimized for prediction performance, size, and relevance.</p></div

Objective scores (smaller are better), prediction accuracy and average probability distance for the validation data (larger are better).

<p>Objective scores (smaller are better), prediction accuracy and average probability distance for the validation data (larger are better).</p

Volcano plot of Plk1-kinase-inhibited cells data.

<p>Volcano plot of the phosphoproteomic data of cells treated with an Plk1 tyrosine kinase inhibitor <i>versus</i> control <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104504#pone.0104504-Oppermann1" target="_blank">[21]</a>. Significantly regulated phosphorylation sites shown in colored circles as identified by MeanRank test, SAM, LIMMA (from left). The two rightmost volcano plots shows differences in detected phosphorylation sites by MeanRank/SAM and MeanRank/LIMMA.</p