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Intergenerational transmission of gender segregation: How parents’ occupational field affects gender differences in field of study choices 下载免费PDF全文
Maaike van der Vleuten Eva Jaspers Ineke Maas Tanja van der Lippe 《British Educational Research Journal》2018,44(2):294-318
The study explores how parents’ occupational field affects gender differences in educational fields. On the one hand, the theory of direct transfer predicts that adolescents enter fields similar to those of their parents because of intergenerational transmission of occupation‐specific resources and that adolescents are more likely to draw upon the resources provided by the higher‐status parent. On the other hand, the theory of sex‐role learning predicts that boys and girls are more likely to choose more gender‐stereotypical fields of study because they learn ‘appropriate’ gender‐role behaviour from their parents’ occupational field and that boys are more likely to learn this behaviour from their father and girls from their mother. We use longitudinal data collected from adolescents and their parents in the Netherlands (N = 2,497) and tested our hypotheses using multiple‐group structural equation modelling and multinomial regression analyses. In line with sex‐role learning, results show that especially mothers who are employed in a more feminine occupational field influence their daughters to enter a more feminine field of study (health, biology, agriculture and veterinary) and their sons to enter a more masculine field of study (science and technology). Mothers’ occupational field therefore not only influences girls’ field of study, but also boys’. This study highlights the role of horizontal characteristics when examining which field of study adolescents enter. Contrary to the stratification literature, which primarily focuses on fathers, this study concludes that mothers play a more important role in gender differences in fields of study. 相似文献
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John J. Frazier Corey D. Stein Eugene Tseytlin Tanja Bekhuis 《Journal of the Medical Library Association》2015,103(1):22-30
Objective:
To support clinical researchers, librarians and informationists may need search filters for particular tasks. Development of filters typically depends on a “gold standard” dataset. This paper describes generalizable methods for creating a gold standard to support future filter development and evaluation using oral squamous cell carcinoma (OSCC) as a case study. OSCC is the most common malignancy affecting the oral cavity. Investigation of biomarkers with potential prognostic utility is an active area of research in OSCC. The methods discussed here should be useful for designing quality search filters in similar domains.Methods:
The authors searched MEDLINE for prognostic studies of OSCC, developed annotation guidelines for screeners, ran three calibration trials before annotating the remaining body of citations, and measured inter-annotator agreement (IAA).Results:
We retrieved 1,818 citations. After calibration, we screened the remaining citations (n = 1,767; 97.2%); IAA was substantial (kappa = 0.76). The dataset has 497 (27.3%) citations representing OSCC studies of potential prognostic biomarkers.Conclusions:
The gold standard dataset is likely to be high quality and useful for future development and evaluation of filters for OSCC studies of potential prognostic biomarkers.Implications:
The methodology we used is generalizable to other domains requiring a reference standard to evaluate the performance of search filters. A gold standard is essential because the labels regarding relevance enable computation of diagnostic metrics, such as sensitivity and specificity. Librarians and informationists with data analysis skills could contribute to developing gold standard datasets and subsequent filters tuned for their patrons'' domains of interest. 相似文献64.
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Sabina Semiz Tanja Dujic Adlija Causevic 《Biochemia medica : ?asopis Hrvatskoga dru?tva medicinskih biokemi?ara / HDMB》2013,23(2):154-171
Type 2 diabetes mellitus (T2DM) is a worldwide epidemic with considerable health and economic consequences. T2DM patients are often treated with more than one drug, including oral antidiabetic drugs (OAD) and drugs used to treat diabetic complications, such as dyslipidemia and hypertension. If genetic testing could be employed to predict treatment outcome, appropriate measures could be taken to treat T2DM more efficiently. Here we provide a review of pharmacogenetic studies focused on OAD and a role of common drug-metabolizing enzymes (DME) and drug-transporters (DT) variants in therapy outcomes. For example, genetic variations of several membrane transporters, including SLC22A1/2 and SLC47A1/2 genes, are implicated in the highly variable glycemic response to metformin, a first-line drug used to treat newly diagnosed T2DM. Furthermore, cytochrome P450 (CYP) enzymes are implicated in variation of sulphonylurea and meglitinide metabolism. Additional variants related to drug target and diabetes risk genes have been also linked to interindividual differences in the efficacy and toxicity of OAD. Thus, in addition to promoting safe and cost-effective individualized diabetes treatment, pharmacogenomics has a great potential to complement current efforts to optimize treatment of diabetes and lead towards its effective and personalized care. 相似文献
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Karen D. Wang Jade Maï Cock Tanja Käser Engin Bumbacher 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(1):192-221
Technology-based, open-ended learning environments (OELEs) can capture detailed information of students' interactions as they work through a task or solve a problem embedded in the environment. This information, in the form of log data, has the potential to provide important insights about the practices adopted by students for scientific inquiry and problem solving. How to parse and analyse the log data to reveal evidence of multifaceted constructs like inquiry and problem solving holds the key to making interactive learning environments useful for assessing students' higher-order competencies. In this paper, we present a systematic review of studies that used log data generated in OELEs to describe, model and assess scientific inquiry and problem solving. We identify and analyse 70 conference proceedings and journal papers published between 2012 and 2021. Our results reveal large variations in OELE and task characteristics, approaches used to extract features from log data and interpretation models used to link features to target constructs. While the educational data mining and learning analytics communities have made progress in leveraging log data to model inquiry and problem solving, multiple barriers still exist to hamper the production of representative, reproducible and generalizable results. Based on the trends identified, we lay out a set of recommendations pertaining to key aspects of the workflow that we believe will help the field develop more systematic approaches to designing and using OELEs for studying how students engage in inquiry and problem-solving practices.
Practitioner notes
What is already known about this topic- Research has shown that technology-based, open-ended learning environments (OELEs) that collect users' interaction data are potentially useful tools for engaging students in practice-based STEM learning.
- More work is needed to identify generalizable principles of how to design OELE tasks to support student learning and how to analyse the log data to assess student performance.
- We identified multiple barriers to the production of sufficiently generalizable and robust results to inform practice, with respect to: (1) the design characteristics of the OELE-based tasks, (2) the target competencies measured, (3) the approaches and techniques used to extract features from log files and (4) the models used to link features to the competencies.
- Based on this analysis, we can provide a series of specific recommendations to inform future research and facilitate the generalizability and interpretability of results:
- Making the data available in open-access repositories, similar to the PISA tasks, for easy access and sharing.
- Defining target practices more precisely to better align task design with target practices and to facilitate between-study comparisons.
- More systematic evaluation of OELE and task designs to improve the psychometric properties of OELE-based measurement tasks and analysis processes.
- Focusing more on internal and external validation of both feature generation processes and statistical models, for example with data from different samples or by systematically varying the analysis methods.
- Using the framework of evidence-centered assessment design, we have identified relevant criteria for organizing and evaluating the diverse body of empirical studies on the topic and that policy makers and practitioners can use for their own further examinations.
- This paper identifies promising research and development areas on the measurement and assessment of higher-order constructs with process data from OELE-based tasks that government agencies and foundations can support.
- Researchers, technologists and assessment designers might find useful the insights and recommendations for how OELEs can enhance science assessment through thoughtful integration of learning theories, task design and data mining techniques.