Meeting the needs of children from birth to three : research evidence and implications for out-of-home provision

In 2001 the Scottish Executive Education Department (SEED) commissioned Professor Colwyn Trevarthen and a team of colleagues to review the research evidence on the development of children from birth to three years old, and to consider the implications of that evidence for the provision of care outwith the home. Incorporating the findings of 30 years of intensive academic research on the development of communication and thinking in infants and toddlers, the review provided an overview of the ways in which adults can contribute to children's development from the earliest stages, the kinds of adult attention and care that are beneficial and the characteristics of out-of-home provision that meets young children's changing needs. This Insight report offers an overview of the conclusions reached in the review, paying particular attention to the implications of the evidence for practitioners and policy makers

Eliciting student teacher's views on educational research to support practice in the modern diverse classroom: a workshop approach

Teachers’ professionalism includes using educational research to support their work in the modern diverse classroom. Student teachers’ views as they enter the profession are therefore important. Within a Higher Education Academy social science priority research strand, ‘Supporting research-informed teacher education in a changing policy environment’, this study developed workshops to ascertain student teachers’ views on educational research, preparing materials suitable for primary and secondary sectors. These could be updated, and used by other higher education courses. Face-to-face or email workshops asked participants about their current uses of educational research, and to read and comment upon one policy research extract and one ‘what works’ research review. Small-scale piloting suggested the workshops readily elicited views, and students identified some personal changes following participation. Participants were generally unfamiliar with the principles of ‘what works’ research. Thematic analysis suggested students considered educational research was often inaccessible, but wanted accessible research to inform their practice

Fake News Detection on Twitter Using Propagation Structures

The growth of social media has revolutionized the way people access information. Although platforms like Facebook and Twitter allow for a quicker, wider and less restricted access to information, they also consist of a breeding ground for the dissemination of fake news. Most of the existing literature on fake news detection on social media proposes user-based or content-based approaches. However, recent research revealed that real and fake news also propagate significantly differently on Twitter. Nonetheless, only a few articles so far have explored the use of propagation features in their detection. Additionally, most of them have based their analysis on a narrow tweet retrieval methodology that only considers tweets to be propagating a news piece if they explicitly contain an URL link to an online news article. By basing our analysis on a broader tweet retrieval methodology that also allows tweets without an URL link to be considered as propagating a news piece, we contribute to fill this research gap and further confirm the potential of using propagation features to detect fake news on Twitter. We firstly show that real news are significantly bigger in size, are spread by users with more followers and less followings, and are actively spread on Twitter for a longer period of time than fake news. Secondly, we achieve an 87% accuracy using a Random Forest Classifier solely trained on propagation features. Lastly, we design a Geometric Deep Learning approach to the problem by building a graph neural network that directly learns on the propagation graphs and achieve an accuracy of 73.3%

Comparative prediction of cardiac events by wall motion, wall motion plus coronary flow reserve, or myocardial perfusion analysis: a multicenter study of contrast stress echocardiography

ObjectivesThis study sought to determine whether the increasing difficulty of assessing wall motion (WM), Doppler coronary flow reserve on the left anterior descending coronary artery (CFR-LAD), and myocardial perfusion (MP) during stress echocardiography (SE) was justified by increasing prognostic information in patients with known or suspected coronary artery disease.BackgroundThe use of echocardiographic contrast agents during SE permits the assessment of both CFR-LAD and MP, but their relative incremental prognostic value is undefined.MethodsThis study followed a multicenter cohort of 718 patients for 16 months after high-dose dipyridamole contrast SE for evaluation of known or suspected coronary artery disease. The ability of WM, CFR-LAD, and MP to predict cardiac events was studied by multivariable models and risk reclassification.ResultsAbnormal SE was detected as a reversible WM abnormality in 18%, reversible MP defect in 27%, and CFR-LAD <2 in 38% of subjects. Fifty cardiac events occurred (annualized event rate 6.0%). A normal MP stress test had a 1-year hard event rate of 1.2%. The C-index of outcomes prediction based on clinical data was improved with MP (p < 0.001) and WM/CFR-LAD (p = 0.037), and MP (p = 0.003) added to clinical and WM data. Net risk reclassification was improved by adding MP (p < 0.001) or CFR-LAD (net reclassification improvement p = 0.001) in addition to clinical and WM data. The model including clinical data, WM/CFR-LAD, and MP performed better than that without MP did (p = 0.012).ConclusionsThe multiparametric assessment of WM, CFR-LAD and MP during stress testing in patients with known or suspected coronary artery disease is feasible. Contrast SE allowed better prognostication, irrespective of the use of CFR-LAD or MP. The addition of either CFR-LAD or MP assessment to standard WM analysis and clinical parameters yielded progressively higher values for the prediction of cardiac events and may be required in today's intensively treated patients undergoing SE, because their average low risk of future cardiac events requires methods with higher predictive sensitivity than that available with standalone WM assessment

Synonymy and stratigraphic ranges of Belemnopsis in the Heterian and Ohauan Stages (Callovian-Tithonian), southwest Auckland, New Zealand.

Belemnopsis stevensi, Belemnopsis maccrawi, and Belemnopsis sp. A (Challinor 1979a) are synonymous; B. stevensi has priority. New belemnite material from Kawhia Harbour and Port Waikato, together with graphical study methods, indicates that many small fragmentary specimens associated with B. stevensi in the lower part of its stratigraphic range are probably the same taxon. B. stevensi has been found only in the Middle and Upper Heterian Stage (Lower Kimmeridgian) at Kawhia and only in the Lower Ohauan Stage (Upper Kimmeridgian) at Port Waikato. This apparently disjunct distribution is attributed to poor exposure in the relevant sections. Belemnopsis kiwiensis n.sp., Belemnopsis cf. sp. B, Belemnopsis sp. B, Belemnopsis sp. D, and Belemnopsis spp. are associated with B. stevensi near the lowest known point in its stratigraphic range. The distribution of stratigraphically useful belemnites within the Heterian and Ohauan Stages is: Conodicoelites spp. (Lower Heterian; correlated with Lower Callovian); Belemnopsis annae (Lower and Middle Heterian; Lower Callovian/Lower Kimmeridgian); Belemnopsis stevensi (Middle Heterian/Lower Ohauan; Kimmeridgian); Belemnopsis keari (Upper Heterian; Kimmeridgian); Belemnopsis trechmanni (Upper Ohauan; Upper Kimmeridgian/Middle Tithonian). The apparently extreme range of Belemnopsis annae remains unexplained. Klondyke Sandstone (new) is recognised as the basal member of Moewaka Formation (Port Waikato area)

FLAP: a framework for linking free-text addresses to the Ordnance Survey Unique Property Reference Number database

Introduction: Linking free-text addresses to unique identifiers in a structural address database [the Ordnance Survey unique property reference number (UPRN) in the United Kingdom (UK)] is a necessary step for downstream geospatial analysis in many digital health systems, e.g., for identification of care home residents, understanding housing transitions in later life, and informing decision making on geographical health and social care resource distribution. However, there is a lack of open-source tools for this task with performance validated in a test data set. Methods: In this article, we propose a generalisable solution (A Framework for Linking free-text Addresses to Ordnance Survey UPRN database, FLAP) based on a machine learning–based matching classifier coupled with a fuzzy aligning algorithm for feature generation with better performance than existing tools. The framework is implemented in Python as an Open Source tool (available at Link). We tested the framework in a real-world scenario of linking individual’s ((Formula presented.)) addresses recorded as free text in the Community Health Index (CHI) of National Health Service (NHS) Tayside and NHS Fife to the Unique Property Reference Number database (UPRN DB). Results: We achieved an adjusted matching accuracy of 0.992 in a test data set randomly sampled ((Formula presented.)) from NHS Tayside and NHS Fife CHI addresses. FLAP showed robustness against input variations including typographical errors, alternative formats, and partially incorrect information. It has also improved usability compared to existing solutions allowing the use of a customised threshold of matching confidence and selection of top (Formula presented.) candidate records. The use of machine learning also provides better adaptability of the tool to new data and enables continuous improvement. Discussion: In conclusion, we have developed a framework, FLAP, for linking free-text UK addresses to the UPRN DB with good performance and usability in a real-world task