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Objectives: The present study investigates the effectiveness of an 8-week mindfulness-based intervention designed to improve educator wellbeing and implemented concurrently in multiple school sites. Methods: Using a cluster (school) randomised controlled design, 185 educators working in 20 Australian schools were randomised to an intervention group (10 schools, number =85, Mean age = 42.34 years) or a control group (10 schools, number =100, Mean age = 43.7 years). Multiple regression analysis was performed to examine effects of intervention on wellbeing and teaching-related outcomes and students’ sense of connectedness to teachers measured post intervention after controlling for the baseline. Results: The intervention predicted lower levels of perceived stress (β= -0.196, p < 0.01, 95% CI [-4.00, -0.92]) and sleep difficulty (β= -0.175, p < 0.05, 95% CI [-7.54, -0.46]), and higher levels of mindfulness (β= 0.252, p Conclusions: The findings suggest that mindfulness-based interventions may contribute to the overall educator wellbeing and this may increase students’ sense of connectedness to teachers without themselves undergoing any intervention.

BackgroundLinkage of administrative data is an important tool for population-based epidemiological analyses, but unlinked or falsely-matched records can lead to selection bias or measurement error, particularly when linkage error disproportionally affects particular subgroups of records. Assessing the potential impact of linkage error on results can be difficult due to the separation of linkage and analysis processes. We aim to describe methods for evaluating linkage, using an exemplar of linked, population-based data for mothers and babies. MethodsWe validated previous linkage of mother-baby records within national hospitalisations data for England (Hospital Episode Statistics). We applied our original linkage algorithm to a subset of gold-standard data to quantify false-matches and missed-matches; we compared characteristics of linked and unlinked data to identify potential sources of bias; and we evaluated how sensitive results of analyses were to changes in the linkage criteria (i.e. to a more conservative linkage algorithm and deterministic linkage only). ResultsOf 72,817 mother-baby records in the gold-standard dataset, 636 (0.9%) were false-matches and 297 (0.4%) were missed-matches using the original linkage algorithm. Records that failed to were more likely to have missing data, and be of lower birthweight and gestational age, or stillbirths. Using a more conservative probabilistic linkage algorithm resulted in fewer false matches (212; 0.3%) but substantially increased the number of missed-matches (7797; 10.7%). Using more conservative probabilistic linkage criteria, or deterministic linkage only, resulted in under-ascertainment of preterm births, reduced precision, and led to bias towards the null in assessing the association between maternal risk-factors and baby outcomes. DiscussionEvaluation of linkage helps us to understand the potential impact of linkage error on results and provides an opportunity to select the most appropriate linkage criteria for a particular analysis. Further research is required to develop methods for identifying and handling bias arising from linkage error. Researchers using linked data should work with data providers to understand linkage quality, as well as other aspects of administrative data quality such as missing data.

With increasing availability of large datasets derived from administrative and other sources, there is an increasing demand for the successful linking of these to provide rich sources of data for further analysis. Variation in the quality of identifiers used to carry out linkage means that existing approaches are often based upon 'probabilistic' models, which are based on a number of assumptions, and can make heavy computational demands. In this paper, we suggest a new approach to classifying record pairs in linkage, based upon weights (scores) derived using a scaling algorithm. The proposed method does not rely on training data, is computationally fast, requires only moderate amounts of storage and has intuitive appeal. Copyright © 2017 John Wiley & Sons, Ltd.

Since 1992, the UK Government has published so-called ‘school league tables’ summarizing the average General Certificate of Secondary Education (GCSE) ‘attainment’ and ‘progress’ made by pupils in each state-funded secondary school in England. While the headline measure of school attainment has remained the percentage of pupils achieving five or more good GCSEs, the headline measure of school progress has changed from ‘value-added’ (2002-2005) to ‘contextual value-added’ (2006-2010) to ‘expected progress’ (2011-2015) to ‘progress 8’ (2016-).This paper charts this evolution with a critical eye. First, we question the Government’s justifications for scrapping contextual value-added. Second, we argue that the current expected progress measure suffers from fundamental design flaws. Third, we show that the differences between expected progress and contextual value added are considerable leading to fundamentally different school rankings. Fourth, we discuss how ‘progress 8’ attempts to address some, but not all, of the flaws in expected progress. We conclude that all these progress measures and school league tables more generally should be viewed with far more scepticism and interpreted far more cautiously than they have often been to date.