In scoping reviews, boundaries of relevant evidence may be initially fuzzy, with refined conceptual understanding of interventions and their proposed mechanisms of action an intended output of the scoping process rather than its starting point. Electronic searches are therefore sensitive, often retrieving very large record sets that are impractical to screen in their entirety. This paper describes methods for applying and evaluating the use of text mining (TM) technologies to reduce impractical screening workload in reviews, using examples of two extremely large-scale scoping reviews of public health evidence (choice architecture (CA) and economic environment (EE)).Electronic searches retrieved >800,000 (CA) and >1 million (EE) records. TM technologies were used to prioritise records for manual screening. TM performance was measured prospectively.TM reduced manual screening workload by 90% (CA) and 88% (EE) compared with conventional screening (absolute reductions of ≈430 000 (CA) and ≈378 000 (EE) records). This study expands an emerging corpus of empirical evidence for the use of TM to expedite study selection in reviews. By reducing screening workload to manageable levels, TM made it possible to assemble and configure large, complex evidence bases that crossed research discipline boundaries. These methods are transferable to other scoping and systematic reviews incorporating conceptual development or explanatory dimensions.

The approach to MECC described here was based on some of the principles outlined in the NICE Guidance on behaviour change published in 2007. The report shows that MECC has considerable potential for changing staff behaviour in relation promoting health enhancing behaviour among members of the general public coming into contact with services.

This study assessed occupational stress amongst 2,638 head teachers of primary and secondary schools, together with principals/directors of further and higher education establishments, throughout the United Kingdom. Data were collected on personal/job demographics, sources of job stress, mental health, job satisfaction and coping strategies. These data were analysed by SPSS-X, producing univariate, bivariate and multivariate techniques. It was found that as we moved from the further/higher education level to secondary to primary sectors, the levels of job dissatisfaction and mental ill health rose. In addition, it was found that, with the exception of primary schools, female head teachers in secondary and FHE seem to be suffering significantly greater job dissatisfaction than their male counterparts, although this does not translate itself into mental ill health. Male head teachers, on the other hand, seem to suffer more mental ill health than their female counterparts. And finally, the two main sources of occupational stress that appear in many of the multivariate analyses as predictors of job dissatisfaction and mental ill health are 'work overload' and 'handling relationships with staff'. The implications of all these findings are discussed in detail.